Patent Application
20100178982
The present invention relates to robotics and/or computer vision, and in some embodiments, to gaming.
The following patent documents and non-patent publications describe potentially relevant background art, and are each incorporated herein by reference in their entirety: U.S. Pat. No. 5,471,312, U.S. Pat. No. 4,753,569, US 20070243914, US 20070293124, US 20020102910, U.S. Pat. No. 6,109,186, U.S. Pat. No. 6,380,844, U.S. Pat. No. 6,780,077, US 20090081923, US 20060111014, U.S. Pat. No. 7,402,106, US 20070097832. US 20030232649, “Camera Calibration using Mobile Robot in Intelligent Space” by Takeshi Sasaki' and Hideki Hashimoto, “Automated Calibration of a Camera Sensor Network” by Ioannis Rekleitis and Gregory Dudek, “Distributed Smart Camera Calibration using Blinking LED” by Michael Koch et al.
No admission has been made that any of the aforementioned documents is a prior art document.
It is now disclosed for the first time a gaming system for providing a gaming service to a user, the gaming system comprising: a. electronic control circuitry; b. a user-directly-controlled self-propelled motorized vehicle (SPMV) operative to move responsively to wirelessly-received user-generated direct commands provided by mechanical motion and/or brainwaves of the user; c. an array of one or more cameras configured to generate an electronic image a scene including the user-directly-controlled SPMV; and d. a computer-directly-controlled SPMV operative to move responsively to computer-generated direct commands that generated by the electronic control circuitry in accordance with: i) one or more game objectives; and ii) a position or orientation, within the electronic image of the scene, of the user-directly-controlled SPMV.
In some embodiments, the electronic control circuitry generates commands to control translational or rotational movement of computer-directly-controlled SPMV in accordance with at least one of: i) a distance between computer-directly-controlled SPMV and/or user-directly-controlled SPMV and a foreign object as determined in accordance with a Euclidian scene reconstruction of the electronic scene image; ii) historical and/or present and/or predicted future contents of a Euclidian world description data structure as determined in accordance with a Euclidian scene reconstruction of the electronic scene image; iii) historical and/or present and/or predicted contents of a game world description data structure.
In some embodiments, the electronic control circuitry includes game strategy circuitry for enforcing one or more of the one or more of the game objectives.
In some embodiments, the electronic control circuitry is operative to: i) detect the user-generated direct commands according to mechanical motion of a user control device or according to a detected gesture of the or a portion thereof; ii) wirelessly transmit the detected commands to the user-directly-controlled SPMV.
In some embodiments, the user control device is selected from the group consisting of a joystick, a mouse, a keyboard, and an accelerometer.
In some embodiments, the electronic control circuitry is operative generate the computer-generated direct commands for controlling the computer-directly-controlled SPMV in accordance with game rules of and/or strategy directives for a game selected from the group consisting of: a) a shooting game; b) a ball game; and c) a hand-to-hand combat game.
In some embodiments, the control electronic circuitry is operative to generate the computer-generated direct commands in accordance with a measured Euclidian distance within the electronic image of the scene between the user-directly-controlled SPMV and foreign object in the scene.
In some embodiments, at least one gaming objective is selected from the group consisting of: a) an objective to score or a goal with a ball or puck; b) an objective to block a goal from being scored with a ball or puck; c) an objective to score or prevent a touchdown or field goal; d) an objective to score a hit against combat game vehicle with a projectile or abeam of light; e) an objective to reduce a probability of a hit being scored against a combat game vehicle with a projectile or a beam of light; and f) an objective to move or grab a game prop with computer SPMV is the game prop is grabbed by user SPMV.
It is now disclosed for the first time a method of operating a self-propelled motorized vehicle (SPMV) including one or more electronically controlled onboard light(s) that effect illumination transition(s) that modifies brightness and/or color of one or more of the onboard lights, the SPMV located within a scene observed by an observing electronic camera, the method comprising: a) obtaining first and second electronic images acquired by the camera, the first image being a pre-transition electronic image IMPPRE describing the before the illumination transition and the second electronic image being a post-transition electronic image IMGPOST describing the SPMV after the illumination transition; and b) comparing the first and second electronic images to determine for each onboard light of one or more of the onboard lights, a respective pixel location within the first and/or second electronic image; c) determining, from the pixel location(s) and camera calibration data for the camera, a respective Euclidian location for each onboard light of the one or more onboard lights; and d) in accordance with the determined Euclidian location(s) of the on-board light(s) electronically controlling rotational and/or translational movement of the SPMV or a portion thereof.
It is now disclosed for the first time a method of controlling a self-propelled motorized vehicle (SPMV) including one or more onboard lights operative to effect an illumination transition that modifies brightness and/or color of one or more of the onboard lights, the method comprising: a) obtaining a time series of images of a scene including the SPMV; b) determining a Euclidian location of the SPMV according to the illumination transition as described by the image time series; and c) controlling rotational and/or translational movement of the SPMV or a portion thereof according to the determined Euclidian location.
It is now disclosed for the first time a method of operating a self-propelled motorized vehicle (SPMV) in accordance with camera calibration data of an electronic camera, the camera calibration data relating pixel-image locations to real-world locations, the SPMV including one or more onboard lights the method comprising: a) electronically controlling the onboard light(s) of the SPMV to induce an illumination transition that modifies brightness and/or color of one or more of the onboard lights; b) comparing first and second electronic images acquired by the camera, the image being a pre-transition electronic image IMGPRE describing the SPMV before the illumination transition and the second electronic image being a post-transition electronic image IMGPOST describing the SPMV after the illumination transition; and c) in accordance with results of the comparing, electronically controlling rotational and/or translational movement of the SPMV or a portion thereof.
In some embodiments, the Euclidian location of the SPMV is determined primarily by analyzing one or more image(s) of the image time series. In some embodiments, the controlling of the rotational and/or translational movement of the SPMV is carried out according to the combination of: i) the Euclidian location of the onboard light(s); and ii) other information derivable from one or more electronic images acquired by the camera.
In some embodiments, the other information describes one or more foreign objects in the scene.
In some embodiments, the foreign object information is selected from the group consisting of color information for the foreign object, surface texture information for the foreign object, and motion information describing translational or rotational motion of the foreign object.
In some embodiments, the controlling of the rotational and/or translational movement of the SPMV is carried out according to the combination of:
In some embodiments, the controlling of the rotational and/or translational movement of the SPMV is carried out according to the combination of: i) the Euclidian location of the onboard light(s); and ii) a stereoscopic description of the scene obtained by analyzing images from multiple observing cameras that view the scene.
It is now disclosed for the first time a system comprising: a) a self-propelled motorized vehicle (SPMV) including one or more onboard lights operative to effect an illumination transition that modifies brightness and/or color of one or more of the onboard lights b) an observing camera operative to acquire a time series of images of a scene including the SPMV; and c) electronic circuitry operative to: i) determine a real-world location of the SPMV according to the illumination transition as described by the image time series and according to camera calibration data for the observing camera that relates pixel-image locations to real-world locations; and ii) control rotational and/or translational movement of the SPMV or a portion thereof according to the determined real-world location of the SPMV.
It is now disclosed for the first time a system comprising: a) a self-propelled motorized vehicle (SPMV) including one or more onboard lights operative to effect an illumination transition that modifies brightness and/or color of one or more of the onboard lights b) an observing camera operative to acquire a time series of images of a scene including the SPMV; and c) control circuitry operative to control rotational and/or translational movement of the SPMV or a portion thereof according to a Euclidian location of the SPMV as determined by an illumination transition as described by the image time series.
In some embodiments, the Euclidian location of the SPMV is determined primarily by analyzing one or more image(s) of the image time series. In some embodiments, the controlling of the rotational and/or translational movement of the SPMV is carried out according to the combination of: i) the Euclidian location of the onboard light(s); and
ii) other information derivable from one or more electronic images acquired by the camera.
In some embodiments, the other information describes one or more foreign objects in the scene.
In some embodiments, the foreign object information is selected from the group consisting of color information for the foreign object, surface texture information for the foreign object, and motion information describing translational or rotational motion of the foreign object.
In some embodiments, the controlling of the rotational and/or translational movement of the SPMV is carried out according to the combination of: i) the Euclidian location of the onboard light(s); and ii) information that is a Euclidian description of a real or virtual boundary in the scene.
In some embodiments, the controlling of the rotational and/or translational movement of the SPMV is carried out according to the combination of: i) the Euclidian location of the onboard light(s); and ii) a stereoscopic description of the scene obtained by analyzing images from multiple observing cameras that view the scene.
It is now disclosed for the first time a method of controlling a self-propelled motorized vehicle (SPMV) including one or more onboard lights operative to sequentially effect a plurality of illumination transitions, each transition modifying brightness and/or color of one or more of the onboard lights, the method comprising: a) obtaining a time series of images of a scene including the SPMV, each image being generated by an observing camera observing the scene; b) computing, according to a first illumination transition set of one or more illumination transitions as described by the image time series, calibration data including at least one of: i) camera calibration data including extrinsic camera calibration data for the observing camera, the camera calibration data relating pixel-image locations to Euclidian locations; ii) SPMV motor calibration data relating SPMV motor energy inputs to Euclidian displacements describing movement of the SPMV or a portion thereof; iii) servo motor calibration data for a servo on which the observing camera is mounted, the servo calibration data relating servo motor energy inputs to perceived Euclidian displacements of the SPMV as perceived by the servo-moved camera moved the servo; c) determining, according to: i) a second illumination transition set of one or more illumination transitions as described by the image time series; and ii) camera and/or SPMV motor and/or servo motor calibration data, a Euclidian location of the SPMV; and d) controlling rotational and/or translational movement of the SPMV or a portion thereof according to the determined Euclidian location.
In some embodiments, the Euclidian location determining is carried out primarily according to images of the image time series.
In some embodiments, i) the calibration data is computed primarily according to analysis of the time series of images; and ii) the analysis includes analyzing illumination transitions of one or more of the onboard lights.
In some embodiments, i) the obtaining of the images of the image time series used for the calibration data computing includes: A) acquiring a calibration set of calibration images that all describe the SPMV in a fixed location in (R, Φ) space which describes relative translational and configurational displacement between camera and SPMV, all images of the calibration set of calibration images being identical except for illumination-transition-associated deviations; and B) effecting image comparison between images of the calibration set of calibration images to compute pixel locations of one or more of the lights at locations associated with image transitions of the calibration set and where images of the calibration set image deviate from each other; and ii) the calibration data is determined in accordance with results of the image subtractions.
In some embodiments, steps (i)(A) and (i)(B) are carried out a plurality of times, each time for a different respective fixed location in (R,Φ) space. In some embodiments, the first and second illumination transition sets are disjoint sets.
In some embodiments, the first and second illumination transition sets are non-disjoint sets.
In some embodiments, the determining of the calibration data including the extrinsic calibration data is an ab initio determining for the extrinsic calibration data.
In some embodiments, the controlling is carried out according to a combination of the determine Euclidian location and other scene information present in image(s) of the time series besides information describing the onboard lighting.
It is now disclosed for the first time a method of providing access for a client device or client application to a self-propelled motorized vehicle (SPMV) that is located in a field of view of an observing camera the method comprising: a) sending one or more commands to the SPMV or to a servo on which the camera is mounted to induce translational or rotational movement of the SPMV or a portion thereof relative to the observing camera b) obtaining a time series of images of a scene including the SPMV, each image being generated by the observing camera and associated with a different location in appearance space (R, Φ, I) for the SPMV that is provided according to the commands of step (a); c) computing, according to differences in the appearance of the SPMV in at least some of the images of the time series, calibration data including at least one of: i) camera calibration data including extrinsic camera calibration data for the observing camera, the camera calibration data relating pixel-image locations to Euclidian locations; ii) SPMV motor calibration data relating SPMV motor energy inputs to Euclidian displacements describing movement of the SPMV or a portion thereof; iii) servo motor calibration data for a servo on which the observing camera is mounted, the servo calibration data relating servo motor energy inputs to perceived Euclidian displacements of the SPMV as perceived by the servo-moved camera moved the servo; d) receiving from the client device or the client application a Euclidian command(s) for the SPMV; e) translating the Euclidian movement command(s) to one or more motor command(s) according to the calibration data; f) sending the motor commands of step (e) to the SPMV; g) performing a Euclidian scene reconstruction of one or more images of the time series according to the calibration data; and h) providing a description of the Euclidian scene reconstruction to the client device or client application. It is now disclosed for the first time a method of providing access for a client device or client application to a self-propelled motorized vehicle (SPMV) including one or more onboard lights operative to sequentially effect a plurality of illumination transitions, each transition modifying brightness and/or color of one or more of the onboard lights, the method comprising: a) obtaining a time series of images of a scene including the SPMV, each image being generated by an observing camera observing the scene; b) computing, according to a illumination transition set of one or more illumination transitions as described by the image time series, calibration data including at least one of: i) camera calibration data including extrinsic camera calibration data for the observing camera, the camera calibration data relating pixel-image locations to Euclidian locations; ii) SPMV motor calibration data relating SPMV motor energy inputs to Euclidian displacements describing movement of the SPMV or a portion thereof; iii) servo motor calibration data for a servo on which the observing camera is mounted, the servo calibration data relating servo motor energy inputs to perceived Euclidian displacements of the SPMV as perceived by the servo-moved camera moved the servo; c) receiving from the client device or the client application a Euclidian command(s) for the SPMV; d) translating the Euclidian movement command(s) to one or more motor command(s) according to the calibration data; e) sending the motor commands of step (d) to the SPMV; f) performing a Euclidian scene reconstruction of one or more images of the time series according to the calibration data; and g) providing a description of the Euclidian scene reconstruction to the client device or client application.
It is now disclosed for the first time a method of providing a gaming service to a user, the gaming system comprising: a. operating a user-directly-controlled self-propelled motorized vehicle (SPMV) to move responsively to wirelessly-received user-generated direct commands provided by mechanical motion and/or brainwaves of the user; b. obtaining a time series of images of a scene including the user-directly-controlled SPMV; and c. providing commands to a computer-generated direct commands in accordance with: i) one or more game objectives; and ii) a position and/orientation, within the electronic image(s) of the scene, of the user-directly-controlled SPMV 120U.
It is now disclosed for the first time a method of computing post-rotation extrinsic camera calibration of a camera that is subjected to a mechanical rotation such that before the motion the camera's field of view is FOVPRE and the camera's extrinsic calibration data is defined by CALIBPRE and after the motion the camera's field of FOVPOST, the method comprising: a) before the camera mechanical rotation, operating the camera to acquire a pre-rotation image IMGPRE associated with FOVPRE; b) after the camera mechanical rotation, operating the camera to acquire a post-rotation image IMGPOST associated with FOVPRE which has an overlap of between 10% and 70% (for example, between 20% and 40% overlap) with FOVPRE; c) if one of the pre-rotation image IMGPRE and the post-rotation image IMGPOST is designated as a first image and the other of the pre-rotation image IMGPRE and the post-rotation image IMGPOST is designated as the second image, for each candidate rotation angle of a plurality of candidate rotation angles: i) respectively applying a virtual-camera-rotation transformation function to virtually rotate the first image, thereby obtaining a respective angular-transformed image; ii) respectively measuring a pixel-match function describing the pixel matchings between at least portion of the respective angular-transformed image and least a portion of the other of the second image; d) according to the measured pixel matching functions, selecting a matching candidate rotation angle describing an estimated value of the mechanical rotation value of the mechanical rotation to which the camera is subjected; and e) computing the post-rotation camera external calibration data for the camera according to: i) the preferred candidate rotation angle; and ii) CALEBPRE which defines the camera's extrinsic calibration data before the rotation.
In some embodiments, i) the camera is mounted on a servo assembly and which subjects the camera to the mechanical rotation according to a delivered power parameter describing delivered power which is delivered to one or more motors of the servo assembly; ii) the candidate rotation angles are selected according to a relationship between the power parameter and an estimated rotation provided by the servo assembly.
In some embodiments, the method further comprises: f) controlling translational and/or rotational motion of an SPMV in a field of view of the camera in accordance with the computed post-rotation camera external calibration data.
It is now disclosed a method of operating a self-propelled motorized vehicle (SPMV) 120 including one or more electronically controlled onboard mechanical shutters 124 that effect mechanical shutter transition(s) that modifies color appearance of a location on SPMV housing, the SPMV located within a scene observed by an observing electronic camera 110, the method comprising: a) obtaining first and second electronic images acquired by the camera, the first image being a pre-transition electronic image IMGPRE describing the SPMV 120 before the mechanical shutter transition and the second electronic image being a post-transition electronic image IMGPOST describing the SPMV 120 after the mechanical shutter transition; and b) comparing the first and second electronic images to determine for each onboard mechanical shutter assembly of one or more of the onboard mechanical shutter assemblies, a respective pixel location within the first and/or second imagec) determining, from the pixel location(s) and camera calibration data for the camera, a respective Euclidian location for each onboard mechanical shutter of the one or more onboard mechanical shutter(s); and d) in accordance with the determined Euclidian location(s) of the on-board mechanical shutter(s), electronically controlling rotational and/or translational movement of the SPMV or a portion thereof.
It is now disclosed for the first time a method of controlling a self-propelled motorized vehicle (SPMV) 120 including one or more onboard mechanical shutter(s) operative to effect an mechanical shutter transition that color appearance of a location on SPMV housing, the method comprising: a) obtaining a time series of images of a scene including the SPMV 120; b) determining a Euclidian location of the SPMV according to the mechanical shutter transition as described by the image time series; and c) controlling rotational and/or translational movement of the SPMV or a portion thereof according to the determined Euclidian location.
It is now disclosed a method of operating a self-propelled motorized vehicle (SPMV) 120 in accordance with camera calibration data of an electronic camera 110, the camera calibration data relating pixel-image locations to real-world locations, the SPMV including one or more mechanical shutters, the method comprising: a) electronically controlling the onboard mechanical shutter(s) of the SPMV 120 to induce an mechanical shutter transition that modifies a color appearance of a location on SPMV housing; b) comparing first and second electronic images acquired by the camera, the image being a pre-transition electronic image IMGPRE describing the SPMV 120 before the mechanical shutter transition and the second electronic image being a post-transition electronic image IMGPOST describing the SPMV 120 after the mechanical shutter transition; and c) in accordance with results of the comparing, electronically controlling rotational and/or translational movement of the SPMV or a portion thereof.
This summary section should not be construed as limiting the invention to any features described in this summary section.
Embodiments of the present invention relate to a system and method for operating a self-propelled motor vehicle (SPMV) according to electronic images of the SPMV acquired by an ‘observer’ electronic camera viewing a scene which includes the SPMV. Some embodiments of the present invention relate to the world of robotic gaming. Other embodiments relate to other applications, including but not limited to manufacturing applications, domestic applications, and agricultural applications.
Human user 102 employs user input device/controller 104 (exemplary controllers include but are not limited to a keyboard or joystick, mobile phone) to generate direct movement commands for user SPMV 120U, and user SPMV moves (i.e. changes its location in R-space or and/or its configuration) in response to the direct movement commands generated by user input device 104.
In the example of
User SPMV 120U includes an onboard wireless receiver for receiving the user-generated (i.e. generated by user input device 104 according to the user's motion or thoughts) commands while computer SPMV 120C includes an onboard wireless receiver for receiving the computer-generated commands.
In one use case, user SPMV 120U controls ball 114 (in sports lingo, SPMV 120U is ‘in possession’ of the ball). In this use case, SPMV 120U moves with ball 114 towards the table on which computer unit 108 is resting. In this use case, to realize one or more of the game objectives listed above, it may be advantageous for computer-directly-controlled SPMV 120C to attempt to “steal” the ball 114 from user-directly-controlled SPMV 120C. Thus, in this use case, software executing on computer unit 108 (i) analyzes images acquired by camera(s) 110 to detect the movement of user-directly-controlled SPMV 120C towards the table; and (ii) in response to this movement by SPMV 120U, in order to attempt to meet the game objective, controls SPMV 120C to move towards the table to increase the likelihood that SPMV 120C could ‘steal’ ball 114 from SPMV 120U.
Although not a requirement, in the non-limiting example of
The system illustrated in
In
A more in-depth discussion of
Some embodiments of the present invention relate to a technique for operating an SPMV that includes one or more on-board lights 124 (for example, LEDs or micro-halogen lights or compact fluorescent lights or any other type of light) (see, for example,
Operating the SPMV includes but is not limited to translating, rotating of the SPMV 120 for any purpose including but not limited to avoiding an obstacle, moving the SPMV 120 in the context of a camera calibration routine, to attempt to fulfill one or more game objectives and for a manufacturing purpose.
In some embodiments, this technique for operating the SPMV (described in more detail below with reference to
In some embodiments, the system is ‘self-sufficient,’ (i) not requiring any special calibration object and (ii) operative with ‘loosely positioned cameras’ whose position may not be known a-priori (for example, placed on a substantially flat surface by a user who is not necessarily a trained technician) (iii) not requiring any additional range or position-detecting technologies such as odometers, IR range finders, ultrasound sonar etc. It is possible to both (i) electronically control SPMV 120 (for example, by wirelessly sending commands) to turn onboard lights 124 on and off or to modify color or brightness of onboard lights 124 and (ii) electronically control translation and/or rotation of SPMV or a component thereof. A series of electronic images are acquired for various ‘lighting states’ and various positions and/or configurations of SPMV. Calibration data for one or more camera(s) 110 may be computed from this series of electronic images.
Furthermore, some embodiments provide routines for calibrating servo assembly 112 and/or a motor of SPMV 120 in a manner that is automatic, does not require any special object, and facilitates a ‘self-sufficient’ system within a minimum number of required components.
In the example of
As noted above, in some embodiment, a servo assembly 112 may be provided to expand the virtual “field of view of camera 110”. Towards this end, some embodiments of the present invention relate to image processing techniques that may be employed even when relatively ‘low-grade’ servos without reliable and/or accurate calibration between electronic command(s) to move the servo and the actual angle that the servo mechanically rotates in response to the electronic command(s).
In the examples of
There is no limitation on the size or shape of SPMV 120 of any other object depicted in the figures. Thus, although the SPMV in
Throughout the figures, figures that refer specifically to game embodiments are labeled with the label (“GE”) and figures that refer to both game embodiments and other game embodiments are labeled with the label “GE and Others.”
In the example of
It is noted that the example of
The systems of
Electronic circuitry 130 may include may include any executable code module (i.e. stored on a computer-readable medium) and/or firmware and/or hardware element(s) including but not limited to field programmable logic array (FPLA) element(s), hard-wired logic element(s), field programmable gate array (FPGA) element(s), and application-specific integrated circuit (ASIC) element(s). Any instruction set architecture may be used including but not limited to reduced instruction set computer (RISC) architecture and/or complex instruction set computer (CISC) architecture. Electronic circuitry 130 may be located in a single location or distributed among a plurality of locations where various circuitry elements may be in wired or wireless electronic communication with each other.
In one non-limiting use case, electronic circuitry 130 includes an executable or library running on a standard PC or laptop within a standard operating system environment such as Windows. Some of the lower level control logic of circuitry 130 may be provided as code stored on the FLASH storage of 8-bit or 32-bit microcontrollers such as Microchip's PIC18 and PIC32 series.
Locations where the electronic circuitry 130 may reside include but are not limited to the housing of any self-propelled vehicle 120, in or on the housing of any camera 110, and in another location (for example, laptop 108 of
Electronic circuitry 130 may be hardwired and/or configured by computer-readable code (for example, as stored in volatile or non-memory) to effects one or more of the tasks: image processing, controlling the motion of any of the SPMVs 120, controlling one or more cameras 110, controlling a servo motor of a servo assembly 112 upon which any camera is mounted (for example, see FIG. 22A)), camera calibration, servo calibration or any other task.
In the example of
In some embodiments, camera electronics assembly 80 may include electronic circuitry for providing functionality related to electronic image acquisition and/or data transfer and/or for any other functionality.
In some embodiments, SPMV electronics assembly 84 may include electronic circuitry for providing functionality related to moving SPMV 120 to translate or rotate SPMV or a portion thereof (for example, by operating a motor, brakes or any other mechanical element) and/or modifying a color or brightness of one or more onboard lights 124 (for example, turning the light(s) on or off) and/or data transfer and/or for any other functionality.
In some embodiments, additional electronics assembly 88 may provide any kind of functionality—in one non-limiting example; at least a portion of additional electronics assembly 88 resides in laptop computer 108. In another non-limiting example, at least a portion of additional electronics assembly 88 resides in user input device 104.
As illustrated in
Any of the components illustrated in
Camera control 180 may regulate when and/or how often images are acquired by camera(s) 110 (for example, by controlling a mechanical or electronic shutter or in any other manner), exposure time or any other parameter related to image acquisition. In one embodiment, camera control 180 does not receive and/or require and/or respond to any instructions from outside of camera electronic assembly 80 (for example, from outside of housing of camera 110). For example, camera 110 may be an ordinary video camera which takes periodically takes pictures and wirelessly transmits (or via a data cable) the contents of the pictures from camera 110 to computer unit 108 or SPMV 120 or to any other electronic device (in this example, there is only outgoing communication from camera 110 to another electronic device without any required incoming communication).
Alternatively, it is possible, in some embodiments, to send instructions to camera control 180 to acquire an electronic image—this may be carried out wirelessly or in a wired communication (for example, via a cable connecting camera 110 to computer unit 108).
For embodiments where one or more cameras 110 are mounted on a servo assembly 112, servo control 178 electrically controls the mechanical movements of servo assembly 112.
As noted throughout this disclosure, any SPMV 120 may move in accordance with contents of electronic image(s) acquired by camera(s) 110. Electronic circuitry 130 includes image processing circuitry 160 for analyzing images. In some embodiments, image processing circuitry 160 is operative to determine a location or orientation of any SPMV 120 and/or any obstacle and/or any prop (for example, a game prop) and/or any other object or visual pattern.
In some embodiments, SPMV 120 includes one or more onboard lights 124 (for example, LEDs or Incandescent, car headlights, halogen, mini-halogen, infra-red lights). This may be useful for determining how to operate any SPMV 120 and/or locating any SPMV 120 or portion thereof and/or for camera or servo or motor calibration. Thus, in some embodiments, electronic circuitry 130 includes light control circuitry 182 for electronically controlling the on-off state and/or the brightness and/or color of onboard light(s) 124. As with any illustrated component (for example, camera control 180 discussed above), in some embodiments, it is wirelessly operate to electronically control onboard light control circuitry 182 (and/or to distribute control onboard light control circuitry 182 across multiple locations in wireless communication with each other). In another embodiment, onboard light control 182 is ‘autonomous’ does not receive and/or require and/or respond to any instructions from outside of control circuitry of SPMV electronic assembly 84.
Image processing circuitry 160 may be configured to analyze contents of electronic image(s) acquired by camera(s) 110 and to determine the locations of one or more objects and/or to measure the distances between objects. As will be discussed below, in some embodiments, the image processing carried out by image processing circuitry 160 includes comparing images taken before and after a so-called illumination transition of one or more on-board lights mounted on any SPMV 120 and controlled by onboard light control circuitry 182. The results of the image comparison may be useful for operating any SPMV 120 (e.g. determining movement commands or any other commands—see the discussion below) and/or determining a location or orientation or configuration of any SPMV 120 or component thereof.
Higher level SPMV control circuitry 196 is operative to determine a plurality of direct commands for any SPMV 120 and/or for issue these commands according to some sort of SPMV operation ‘strategy.’ In one example related to
In an example related to
Thus, in some embodiments, higher level SPMV control circuitry 196 may operate according to the contents of Euclidian world description data structure 144 describing the Euclidian location(s) of one or more objects within the scene including but not limited to any SPMV 120 and/or any obstacle (for example, the table in
In some embodiments, SPMV 120 may include one or more onboard mechanical appendages (such as a coffee dispensing assembly or a robotic arm or robotic hand or robotic forklift) and/or any other onboard accessory (such as an onboard laser—for example, for a cutting or welding robot or for a ‘laser tag’ game robot). Thus, in some embodiments, electronic circuitry 130 includes appendage/onboard accessory control circuitry 142 (there is no requirement that all circuitry 142 itself be ‘onboard’ on the SPMV 120 though this is an option—the term ‘onboard’ refers to the appendage or the accessory controlled by the appendage/onboard accessory control circuitry 142).
As will be discussed below, some embodiments, it is useful to effect a calibration of camera 110 and/or servo assembly 112 and/or onboard motor (i.e. for translating, re-orientating or changing a configuration of SPMV 120 or a portion thereof) of SPMV 120. Thus, in some embodiments, electronic circuitry 130 includes circuitry for calibration of any camera 110 and/or servo assembly 112 and/or onboard motor of any SPMV 120.
Any component or combination of components of
It is appreciated that
Computer memory 132 (i.e. shown in
Although the memory and computer processors are shown as single elements in
For convenience, in the context of the description herein, various terms are presented here. To the extent that definitions are provided, explicitly or implicitly, here or elsewhere in this application, such definitions are understood to be consistent with the usage of the defined terms by those of skill in the pertinent art(s). Furthermore, such definitions are to be construed in the broadest possible sense consistent with such usage.
In a Euclidean representation of the real world, the representation accurately records the distances, angles, volumes. Parallel lines in the real world are represented as parallel lines.
In other representations, such as affine or projective, distances may be preserved but angles not. Parallel lines may not be parallel etc. They may nevertheless be able to accurately represent “closer/further than” relations between two points.
Many scene reconstruction techniques cannot create Euclidean representations. These techniques may reconstruct a scene object (e.g. house) as distorted by shears and different sizes in different directions.
For the present disclosure, when a quantity or some property is ‘determined’ there is no requirement this quantity or property to be determined exactly, and it may be determined, for example, only approximately.
For the present disclosure, when a quantity or some property is determined or computed or calculated ‘in accordance’ (or ‘according to’) with some sort of parameter, there is no requirement that the quantity or property be determined exclusively in accordance with the parameter—rather, it is meant that the quantity or property is determined in accordance with “at least in part.’
A Discussion of Some Gaming Embodiments with Reference to
In step S151, the user SPMV 120A receives from user control device 104 via a wireless link (for example, a radio link or an IR link or any other wireless link) one or more direct game commands instructing the remote-control user SPMV 120A to effect one or more game operations. Game operations include but are not limited to commands to accelerate, decelerate, turn, illuminate a light mounted to the SPMV (for example, an LED or a laser light for ‘firing’ at an opponent such as a tank), move to a particular location, and move a robotic arm or leg. The direct game command is generated by user control device 104 in accordance with user input—for example, in response to a user pressing of a button, a user moving the user control device 104, turning a rotational object such as a knob or wheel, a user generating brainwaves, or any other user mechanical or electrical activity. In another example, user control device 104 may be operative to detect human hand gestures (or gestures of any other human body part) even when the hand is not in contact with user control device 104.
In step S155, in response to the game command(s) received in step S151, the user remote-controlled SPMV 1201 effects one or more of the operations described by the game commands—for example, by moving one or more wheels or robotic arms or legs, effecting a steering system operation, firing a projectile, or any other operation specified by the user whose activity is detected by user control device 104.
In step S159, the scene including the remote-control user SPMV 120A is imaged by camera(s) 110. By electronically analyzing the image(s), it is possible to determine the Euclidian location and/or orientation of any object within the scene, based upon camera calibration data. These objects include but are not limited to (i) user-controlled 120U and/or computer-controlled/opponent 120C SPMV and/or (ii) one or more game objects (for example, a ball) and/or (iii) one or more ‘environmental’ objects such as walls or other boundaries, or obstacles (for example, on the floor).
Typically, step S159 is carried out repeatedly (for example, camera 110 may be a video camera)—for example, at least several times a second. At each point in time, the physical scene may change, and some sort of ‘real world data structure’ maintained in accordance with Euclidian scene reconstruction operations.
In step S163, one or more data storages are updated to reflect the updated physical ‘real-world’ reality and/or ‘game-world’ reality according to the data input received via observing camera(s) 110 and according to the Euclidian scene reconstruction. In one example, one or more SPMV(s) have moved or rotated and their new positions are stored in Euclidian real world description data structure 144 (described below with reference to
In step S167, computer SPMV 120C responds, in accordance with (i) one or more game objective(s); and (ii) the contents of the Euclidian-reconstructed scene which is acquired by observing camera(s) 110.
As will be discussed below, in some embodiments of the present invention, a ‘game strategy engine’ is provided for facilitating control of SPMV 120C.
In one example, if user SPMV 120U approaches goalpost 118C, computer SPMV 120C may respond by approaching the goalpost in order to ‘block a shot.’ In this example, the Euclidian scene reconstruction as indicated by contents of real world description data storage 144 may be read by the game strategy engine, which may be involved in generating a command to computer SPMV 120C to move towards the goalpost 118C.
In some embodiments, computer SPMV 120C is configured to operate according to the combination of (i) information related to locations and orientations of scene objects that is stored in Euclidian real world description data storage 144; and (ii) additional information—for example, game data beyond merely the location and/or orientation of various objects (e.g. SPMV(s) or game props or other objects) or boundaries.
Thus, in one example), the amount of time of the soccer game is recorded, and the player (i.e. either the ‘computer’ player or the ‘user’ player) with the higher ‘score’ wins. In this example, if the ‘computer’ player is ‘in the lead’ (i.e. had a higher score), the game strategy engine or game strategy circuitry (see element 170 of
Information such as the score of the game, the remaining time (i.e. for timed games), the number of previous fouls per player (for a robotic basketball game) may be stored in game world description data storage 146 (see
The example of
In some embodiments related to gaming, electronic circuitry 130 may include some or all components of
Electronic circuitry 130 further may include game logic engine 150 (this is virtual world information) for enforcing game rules. Game logic engine 150 may maintain update game world description data storage 146 according to the set of game rules. One example of a game rule for robot soccer is that if the ball crosses the plane of the goal a team is awarded one point. In this case, an entry in a data structure of game world description repository 144 indicating the score of the game may be updated game logic engine 150.
One example of a game rule (i.e. that may be enforced by game logic circuitry) related to robot basketball is if a player (i.e. implemented as a SPMV 120) in possession of the ball goes out-of-bounds, then play is frozen, the clock is stopped, and the other team is to be awarded possession of the ball. In this case, an entry in a data structure describing the amount of time remaining in the game, or who is entitled to receive the ball from a robotic referee, may be updated.
In some embodiments, the game rules enforced by game logic engine 150 may relate to informing or alerting a player that a specific game event has occurred. Game logic engine 150 may determine whether or not a game event (for example, scoring a goal, hitting a tank, etc) has occurred. In one use case, a display screen and/or speaker to present information to the user may be provided (for example, as part of game controller 104), and the user may be alerted via the screen and/or speaker. In one example, a player alert engine 152 sends the information, describing the game event, to the user.
Game strategy circuitry 170 may access game world description data storage 146 and/or real world description data storage 144 when making the strategic decisions for operating computer SPMV 120C according to game objective(s) (see the discussion in the previous section).
Thus, in some embodiments, opponent or computer SPMV 120C may be configured to operate according to a game strategy for attempting to achieve one or more game objectives. In one example related to a shooting game (for example, tanks), this game strategy may include (i) moving computer SPMV 120C away from locations where user SPMV 120U can possibility fire upon opponent/computer controlled SPMV 120B; and/or (ii) re-locating or re-orienting computer SPMV 120C so that it is possible to successfully fire upon user SPMV 120U.
According to a game strategy, game strategy engine 170 may issue a set of commands for one or more computer SPMV(s) 120C. In one example related to a robotic soccer game, if game strategy engine 170 detects from the contents of game world description repository 144 that the soccer ball 114 is about to cross the plane of the “computer's” goal (as opposed to the user's/player's goal), game strategy engine 170 may respond by issuing a command to the “computer's/opponent's” goalie robot (which is a SPMV 120B) to move to attempt to intercept the soccer ball before it crosses the plane of the goal.
In one example, this command may be sent wirelessly to opponent/computer-controlled SPMV 120C via a wireless link. In one particular example related to
Any component or combination of components
Direct commands to a mechanical robotic appendage (for example, a tank turret or a robotic arm or foot) include: (i) move appendage left; (ii) move appendage right; (iii) kick (for a robotic foot); (iv) accelerate (or decelerate) movement of appendage to the left (or right); (v) grab (for a robotic hand); (vi) rotate clockwise to a specific angle; (vii) rotate anti-clockwise to a specific angle; (viii) rotate at a certain angular velocity; (vi) release (for a robotic hand).
Direct commands to ‘fire’ for shooting games include but are not limited to (i) a command to fire (either a project, or a ‘light’ such as a laser for games like laser tag); (ii) a command to accelerate (for decelerate) a rate of repetitive firing; and (iii) a command to cease firing.
In step S511 the user input device 104 detects movements or brainwaves of the user 102 (for example, touching or moving a finger across a touch-screen, pressing a mouse-button, pressing a key on a laptop keyboard, moving a mouse or the stick of a joystick user 102 hand or body movements captured by a dedicated user-input camera etc) to generate a user-descriptive electrical signal describing the user activity. This signal may be translated/converted, in step S515, into a direct command for operating (for example, for moving) user SPMV 120U.
This electronic conversion of the user-descriptive electrical signal into direct commands may be carried out, at least in part, in any location, including but not limited to electronics assembly of user input device 104, electronics assembly 88 of laptop 108 (or desktop or any other ‘external’ digital compute that is external to camera(s) 110, the SPMVs 120 and the user input device 104), in electronics assembly 84 of any SPMV 120, or in any location.
In some embodiments (not illustrated in
In the specific example of
In some embodiments, it is possible to retain (i.e. in volatile or non-volatile memory of computer unit 108) information about the direct commands that are routed from user 102 to user SPMV 120U via computer unit 108. For example, this information (i.e. describing direct command data) may be entered into to real world description Data structure 144 (see
In yet another example, the ‘translation’ of user mechanical engagements of (or brainwaves) input device 104 into commands for user SPMV 120U may be carried out within input device 104, and these commands may be wirelessly transmitted by input device 104. In yet another example, user SPMV 120U (for example, circuitry of SPMV electronics assembly 84) may (i) receive the wireless user electronic signal describing user mechanical engagement of input device 104 and (ii) electronically convert or translate this signal into commands for moving SPMV 120U.
In
In step S619, direct commands are generated S619 for computer SPMV (i) in accordance with game objective(s), the Euclidan state of the scene and/or data of an additional game data structure and; (ii) in response to the detected movement of any object in the scene (e.g. user SPMV, game prop such as ball 114 or any other object). In some embodiments, the movement is detected by analyzing electronic image(s) and effecting multiple Euclidian scene reconstructions (a first scene reconstruction at a first time a second reconstruction at a later time)—it is possible to detect movement or angular displacement according to the two scene reconstructions.
In step S623, the computer SPMV 120C effects the direct command (for example, to move, to fire, etc)—for example, according to the output of game strategy circuitry.
Although the user's moves of
Embodiments of the present invention relate to a SPMV 120 that includes a plurality of onboard lights (for example, see elements 124A-124D of
In some embodiments, the SPMV 120 may be operated primarily according to information provided in images generated by observing camera 110—for example, according to a Euclidian location of SPMV 120 (or a portion thereof). In some embodiments, this may be obviate the need for an additional location-finding system (i.e. other than a system based on images of the observing camera 120), such as an ultrasound locating system or a radar-based locating system. In some embodiments, the SPMV 120 is operated in accordance with (i) the Euclidian location of SPMV (or a portion thereof) as determined according to illumination transitions and (ii) other information available in the scene including the SPMV that is described by the image acquired by observing camera 110.
Before explaining routines for utilizing illumination transitions to operate SPMV 120 (i.e. control translational or rotational movement of SPMV or of a portion thereof), several concepts are now explained: illumination transitions (see
This ‘self-sufficient’ approach obviates the need for a special calibration object (i.e. or the need to effect any calibration procedure that requires a relatively ‘high’ degree of technical competence by user 102) and allows for the distribution of relatively simple systems that employ a minimal number of components.
Thus, in some embodiments (for example, see
Before describing the technique for controlling movement of robotic SPMVs 120 according to ‘illumination transitions’ of one or more onboard lights 124 mounted onto an SPMV 120, it is useful to discuss the concept of illumination transitions. This concept will first be explained relative to a simple non-limiting example illustrated in
In
By turning on the light 124A, SPMV 120 is said to undergo an illumination transition TRANS1 between the time of FRAME 1 and the time of FRAME 2 (and illumination transition TRANS1 relates only to a single light 124A); by simultaneously turning off light 124A and turning on light 124B, SPMV 120 is said to undergo an illumination transition TRANS2 between the time of FRAME 2 and the time of FRAME 3 (and illumination transition TRANS2 relates to lights 124A and 124B); by simultaneously turning off light 124B and turning on light 124C, SPMV 120 is said to undergo an illumination transition TRANS3 between the time of FRAME 3 and the time of FRAME 4 (and illumination transition TRANS3 relates to lights 124B and 124C); by simultaneously turning off light 124C and turning on light 124D, SPMV 120 is said to undergo an illumination transition TRANS4 between the time of FRAME 4 and the time of FRAME 5 (and illumination transition TRANS4 relates to lights 124C and 124D).
In the simplified example of
By effecting a subtraction between frames, it is possible to determine a pixel location of one or more onboard lights 124. In the example of
The “difference images” are illustrated in FIG. 11B—these images are obtained by subtracting one frame from another frame, for example on a ‘pixel-by-pixel’ basis (e.g. it is possible to subtract the intensity of each pixel in a first frame/image from the intensity of the corresponding pixel in a second frame/image). As is illustrated in
In the event that the images of any image pair whose constitutive images are compared substantially identical images (i.e. identical in all aspects except for appearance deviations associated with a illumination transition(s)), it may be useful to subtract images when comparing a pair of images (IMGPRE, IMGPOST), where IMGPRE describes the appearance of the SPMV including the onboard lights 124 before a given illumination transition, and IMGPOST describes the appearance of the SPMV including the onboard lights 124 after a given illumination transition—for the example of TRANS1, it is possible to write that FRAME 1 is a IMGPRE and FRAME 2 is a IMGPOST.
The deviation between frames (illustrated in
For the present disclosure, a “pixel location” refers to either (i) a single pixel; (ii) a portion of a single pixel; and (iii) a centroid of a group of more than one pixel (for example, weighted by brightness or darkness or color). The “pixel location” refers to a position in the image space and not to a position in world space.
In some embodiments, it may be preferred that the length and width of the cloud of pixels are on the same order of magnitude.
As noted above, the “image comparison” routine employed in
Motion detection or motion estimation techniques are well-known in the art and are used extensively in, for example, video compression.
It is possible to subtract this transformed image (i.e. transformed to reverse a change in location/orientation between camera 110 and SPMV 120—for example, due to mechanical motion of camera 110 or SPMV 120) from one of IMGPRE and IMGPOST in order to obtain a pixel location of one or more onboard lights.
As will be discussed below with reference to
In some embodiments, because the mapping between “points in pixel space” on the left hand side of
The skilled artisan is referred to the section “Details of a Specific Exemplary Embodiment” below for additional details about how non-limiting examples for computing the real-world Euclidian location from a pixel location.
In
In step S901 of
In step S905 of
In step S909 of
In step S913 of
The image of step S901 is acquired at time t1, and describes the appearance of SPMV before the illumination transition which occurs at time t3—therefore, the image of step S901 is referred to as IMGPRE. Before the illumination transition which occurs at time t3 in step S909 illustrated in
The image of step S913 is acquired at time t4, and describes the appearance of SPMV after the illumination transition which occurs at time t3—therefore, the image of step S913 is referred to as IMGPOST. After the illumination transition which occurs at time t3 in step S909 illustrated in
In one example, IMGPRE and IMGPOST are substantially identical except for features related to the illumination transition (i.e. IMGPRE describes illumination state It and IMGPOST describes illumination state I2).
In step S917 of
In step S923 of
In step S931 of
In one use-case related to step S931 (this relates to ‘example 1’ of
In another use-case related to step S931 and
Another use case (this relates to ‘example 1’, of
Another use case (this relates to ‘example 1’ of
Another use case relates to motion control. In this example, an SPMV 120 is attempting to move from a first location (for example, computer goalpost 118C of
According to the second example (see the right hand side of
In some of the above examples, the ‘operating’ of step S931 included sending a wireless command to SPMV 120. However, this is not a limitation. In yet other embodiments, the command may be sent via a data cable. In yet other embodiments, one or more of steps S905 and/or S931 are carried out within SPMV electronic assembly 84, and the command may be generated within SPMV 120.
As is evident from the examples discussed above, in some embodiments, in step S931 the SPMV 120 may be operated (e.g. to control rotational or translational movement of SPMV of a portion thereof) according to a Euclidian relationship (for example, a distanced between, an angle between, a rate of change of distance or angle, etc) between (i) SPMV 120 (or a location on SPMV 120); and (ii) a ‘foreign object’ other than SPMV 120 (for example, another SPMV or a prop or any other object) and/or a Euclidian location or locations (for example, a boundary such as the ‘out-of-bounds’ boundary 96 in the soccer game depicted in
Thus, some embodiments relate to utilizing the combination of (i) the Euclidian location of one or more lights that undergo an illumination transition as described in a plurality of images of the scene including the SPMV 120 having onboard lights 124; and (ii) other information describing other objects in the scene as recorded in the electronic image(s) acquired by observing camera 110.
Examples of the ‘other information’ includes but is not limited to location or orientation information for the other object, shape information describing the foreign object or boundary, height information describing the foreign object or boundary, color or surface texture information for the foreign object, and motion information describing translational or rotational motion of the foreign object.
In some non-limiting embodiments, in order to detect the location or any other aspect of the ‘foreign object’ within the scene image including SPMV 120 acquired by the observing camera(s), one or more the following techniques may be employed (i.e. either any single technique or any combination or multiple techniques):
A) information from a plurality of cameras 110 viewing the same scene (for example, cameras 110A and 110B of
B) in some embodiments, it may be difficult to detect where a given foreign object is (e.g. because it is a ‘difficult’ image processing problem). Nevertheless, if the foreign object (e.g. ball 114 in
C) manual input for the user—in some embodiments, the user may manually provide information describing the real-world location of various foreign object (i.e. obstacles) in the scene—for example, describing the real-world size of the foreign objects and/or the real-world distance between the foreign objects and/or a pre-determined real-world trajectory in which any foreign object will translate or rotate. Thus, in some embodiments, step S931 is also carried out in accordance with this information.
In a non-limiting example, a software application executing on a computer 108 may provide a user interface for receiving this manual input. This user interface may include a visual description of the scene either as (i) the image taken by the camera unmodified (ii) a combination of images stitched together as is known in the art (iii) a 3D graphic representation of the room as calculated from the stereoscopic or other calculation. The user may then be prompted to draw on the screen locations or lines or interest or may be prompted to respond to a question regarding a highlighted area.
In steps S901 and S913, images are acquired by observing camera(s) 110. In some embodiments, an explicit command may be sent from computer unit 108 to camera 110 for the purpose of acquiring the image at the appropriate time. After the command of step S901 is sent computer unit 108 to camera 110 to acquire IMGPRE, a different command is sent from computer unit 108 in step S905 to induce the illumination transition. After time is allowed for the illumination transition to be carried out at SPMV 120 (or alternatively, after an acknowledgment of the illumination transition is received back), in step S913 it is possible to send an additional command to camera 110 (for example, in response to an acknowledgement of the illumination transition wirelessly received by computer unit 108 from SPMV 120—thus the image acquisition of step S913 may be in response to the illumination transition).
Thus, the ‘command scheme’ implementation for instruction the camera discussed with reference to
It is noted, however, that there is no requirement that commands be provided to camera 110 and/or onboard lighting 124.
In one non-limiting use case, camera 110 may be a video camera and each image may be associated with a time stamp. In this use case, computer unit 108 (or any other electronic circuitry or logic managing the process of
In some embodiments, the illumination transition time may be determined according to the time a command is sent to SPMV (for example, see step S905 of
Alternatively, there is no requirement to send a wired, or wireless command to induce the illumination transition. In some embodiments, onboard lights 124 may be ‘internally configured’ (for example, SPMV electronics assembly 84 may be internally configured without requiring any external input from outside of SPMV 120) to automatically undergo one or more illumination transitions—for example, at pre-determined times (e.g. according to some periodic blinking pattern other with some other pre-determined temporal scheme).
In one example when it is ‘known’ or ‘predicted’ that an image transition will occur at a particular time tgiven, then it is possible to acquire in IMGPRE and/or IMGPOST ‘response’ to this information about the timing of the illumination transition at tgiven. In another example, camera(s) 110 may be configured to repeatedly acquire images of the scene (for example, in ‘video camera mode’) and images may be designated as IMGPRE and/or IMGPOST according to time stamps of the image and information about the illumination transition at tgiven.
The routine of
In step S711, camera calibration data including extrinsic calibration data (see, for example, the map of
In step S715, one or more onboard lights 124 of SPMV 120 are electronically controlled to modify color and/or brightness to effect an illumination transition trans. In one example, this is carried out by sending wireless commands (see steps S905-S909 of
In step S719, a time series of images is acquired, including IMGPRE and/IMGPOST. In some embodiments, this is carried out in steps S901 and S913 of
Reference is now made to
Instead of requiring an external calibration object and/or the presence of trained technicians, the systems of embodiments of
Thus, in step S1011 of
The skilled artisan is referred to the section “Details of a Specific Exemplary Embodiment” below for additional details about how non-limiting examples for computing the real-world Euclidian location from a pixel location.
The routines of
In
The techniques of
Nevertheless, experiments conducted by the present inventor have indicated that the quality of calibration may be improved (even dramatically, in some situations) by mechanically moving SPMV 120 and/or camera 110 (for example, using servo assembly 112) during calibration. Techniques for carrying this out are discussed below with reference to
(R,Φ) space is the Cartesian product of R space and Φ-space.
Motion in (R,Φ) space (see step S1223 of
Any combinations of the motions modes may be provided (for example, in step S1223).
In step S1015′, it is discussed that it is possible to generate servo calibration data and to utilize this calibration data when determining a Euclidian location of SPMV.
In the present sections, servo assemblies 112 are discussed for the particular non-limiting case where there are two cameras 110 each camera being mounted on a respective servo assembly. In other embodiments, there may only be a single camera mounted on a single servo assembly. In other embodiments (not shown), in order to save manufacturing costs, it is possible to provide multiple cameras on a single servo assembly.
Thus, as is illustrated in
In one implementation, both the camera 110 and the pan-and-tilt system (referred to throughout the present disclosure as a ‘servo assembly’) 112 are controlled and powered by a first camera control unit (CCU) 5 (e.g. corresponding to camera electronics assembly 80) to which the camera and servo are attached by direct wiring. The CCU 5 (or camera electronics assembly 80) is an example of electronic control circuitry 130 and may be implemented in any combination of hardware, software and firmware. In the exemplary embodiment the CCU 5 may include some basic computational capability in the form of microcontrollers and memory. In some embodiments, CCU 5 is capable of providing the signals required to direct the servo motors 3 and 4 of servo assembly 112 and the camera 110.
In some embodiments, CCU 5 may include volatile and/or non-volatile memory for storing an image acquired by camera 110. For example, the CCU 5 may receive the camera image, process it to some extent and capable of storing the processed or unprocessed image (in and before communicating data of the image (i.e. either wireless communication as shown in
A second camera assembly 92B may include camera 110B placed on a second pan-and-tilt system 112B (or ‘servo assembly’) which operates in the same manner as the as the first pan-and-tilt system 112A. Both the camera 110B and the pan-and-tilt system 112 may be controlled and powered by a second CCU 8. Thus, the second camera assembly 92B may include the second camera 110A, the second pan and tilt system 112B and the second CCU 8.
In one non-limiting embodiment the two CCUs 5 and 8 may be each connected by a data cable (for example a USB cable each) to a computer unit 108. However, in other embodiments the cable may be replaced by a wireless connection with similar capabilities. In yet other embodiments the computer may be replaced be a mobile computing device, a dedicated interface unit, a router connected to a remote computer or network or any similar alternative. Any such controlling processing unit will be referred to without precision as: the controlling computer. In any case the cable or wireless interface is used to transmit images from the camera to the controlling computer and is also used for the controlling computer to transmit instructions to the CCU.
A Discussion of Servo Motor Calibration Data with Reference to
In some embodiments, any component of servo assembly 112 may be relatively inexpensive and/or not completely reliable. As such, it is possible that when an image is acquired, that the location of SPMV 120 in (R,Φ) space relative to camera 110 is not known or known with uncertainty.
For example, if the location is known at a certain point in time, but then camera is moved (for example, tilted) by an unknown (or known with relatively low certainty) angle, then at the later time the relative location or orientation of SPMV 120 relative to camera 110 may be uncertain. Towards this end, it may be useful to have ‘servo calibration data.’ Servo calibration data describes the relationship between: (i) an input to motor(s) of servo assembly 112 (for example, a voltage or during that a voltage is applied or any other input)—this is the ‘x’ axis of
If (i) before servo movement the position of camera 110 is known; and (ii) the amount of camera displacement (e.g. angular displacement) associated with a given amount of power is known (e.g. from the graph of
It is noted that the Euclidian location of SPMV is always determined relative to the camera 110 (this may also be determined in an absolute level if the location of the camera 110 is known). Thus, knowledge about the ‘Euclidian location’ of SPMV (for example, in FIG. 18B—for example, in accordance with determined pixel locations of lights that are deployed with known geometry) gained by (i) effecting illumination transitions, (ii) acquiring PRE and POST images; and (iii) comparing the PRE and POST images to learn about pixel location and hence Euclidian location of light(s), may be useful for also helping to determine information the location of camera 110 (and hence information about a previous camera displacement caused by servo motors). For example, it is possible to leave SPMV 120 stationary and to move camera 110 using servo assembly 112.
Thus, the combination of (i) knowing about ‘power parameters’ for ‘input power’ to motor(s) of servo assembly 112; and (ii) knowing about how much camera 110 has moved relative to SPMV 112 since camera 110 was at a previous known position (i.e. from knowledge of the Euclidian location of SPMV 112 relative to camera 110 which is determined from pixel locations of lights) can be useful for computing servo motor calibration data in step S1015′. Since it is possible to obtain information about the ‘current location’ of SPMV 120 in (R,PHI) space from knowledge of the Euclidian location of lights 124 (i.e. based on pixel locations of lights), comparing images in accordance with an illumination transitions may be useful for (i) determining camera 110 has moved or re-oriented since servo motor(s) were subjected to known power parameters (e.g. input voltages); and (ii) thus, determining information related to the servo calibration curve (see
At a later time, it is possible that camera 110 has moved again (i.e. since the most recent calibration). In this case, it may be useful to use the servo calibration information (e.g. as in
A Discussion of SPMV Onboard Motor Calculation with Reference to
Similar reasoning may be used for the onboard motor(s) of SPMV 120.
Thus, embodiments of the present invention relate to techniques for (i) determining the relative Euclidian location in (R,Φ) space of SPMV 120 relative to camera 110 and (ii) operating SPMV 120 according to this knowledge of the Euclidian location in (R,Φ) space.
In some embodiments, when power is delivered to SPMV 120 onboard motors, it is not certain how far SPMV 120 moves—this is especially true if SPMV 120 includes inexpensive or not completely reliable components. In another example, the friction parameters between SPMV 120 and the floor may not be known, or may vary depending on the surface of the flooring (e.g. carpet may be different from wood flooring). Since it is desired to be able to determine the relative Euclidian location in (R,Φ) space, it is desired to know, after a known amount of voltage (or any other power parameter) is delivered to SPMV 120 onboard motors, how much the SPMV (or a portion thereof) has moved or re-oriented in response to the delivery of power to SPMV onboard motors.
This relationship is illustrated in
If (i) before SPMV movement the position of SPMV 120 relative to camera 110 is known; and (ii) the amount of SPMV displacement (e.g. translational or angular displacement) associated with a given amount of power is known (e.g. from the graph of
It is noted that the Euclidian location of SPMV is always determined relative to the camera 110 (this may also be determined in an absolute level if the location of the camera 110 is known). Thus, knowledge about the ‘Euclidian location’ of SPMV (for example, in FIG. 18B—for example, in accordance with determined pixel locations of lights that are deployed with known geometry) gained by (i) effecting illumination transitions, (ii) acquiring PRE and POST images; and (iii) comparing the PRE and POST images to learn about pixel location and also Euclidian location, may be useful for also helping to determine information of SPMV 120 relative to the camera, and hence information about how far SPMV 120 has traveled since its position was last known and since SPMV onboard motors have operated.
Thus, the combination of (i) knowing about ‘power parameters’ for ‘input power’ to onboard motor(s) of SPMV 120 and (ii) knowing about how far SPMV 120 has moved or reoriented since SPMV 120 was in a previous known position in (R,Φ) space relative to camera 110 can be useful for computing onboard SPMV motor calibration data in step S1015′. Since it is possible to obtain information about the ‘current location’ of SPMV 120 from knowledge of the Euclidian location of lights 124 (i.e. based on pixel locations of lights), comparing images in accordance with an illumination transitions may be useful for (i) determining how far an SPMV 120 has traveled since being subjected to known power parameters (e.g. input voltages); and (ii) thus, determining information related to the SPMV onboard motor calibration curve (see
At a later time, it is possible that SPMV 120 has moved again (i.e. since the most recent calibration). In this case, it may be useful to use the SPMV onboard motor calibration information (e.g. as in
As noted above, knowledge of the servo motor and/or onboard SPMV motor calibration curve (see
In
Similarly, in
A Discussion of Operating Robots in an Outdoors Environment and/or an Environment with a Relatively Large Ambient Light Level
In some embodiments, when there is a relatively ‘high’ level of ambient light, one or more onboard lights 124 may be difficult to detect from images acquired by camera 110. In this case, (or in other cases), it may be desirable to replace any onboard lights with onboard mechanical shutters.
For example, it is possible to have a black mechanical shutter (on SPMV housing which conceals a white surface. The local surface of SPMV 120 housing in the vicinity of the black mechanical shutter is also black. Thus, when the shutter is closed, the entire surface (i.e. including the shutter itself and the surrounding region) appears black—conceptually, this may be regarded as similar to a ‘light off’ illumination status. When the shutter is open, the surround region remains black but the viewed surface of SPMV 120 housing at the location of the shutter is white.
In
As is shown in
Thus, in some embodiments, it is possible to substitute for any onboard light 124 the combination of (i) specially colored shutter which may conceal a region of a different color as discussed with reference to
In some embodiments, this may be preferred in an outdoor environment.
Some embodiments of the present invention relate to a scene reconstruction translation layer 952 (for example, a software translation layer which resides in volatile or non-volatile memory and is executing by electronic circuitry such as a computer processor). The software translation layer 952 (i) receives Euclidian commands for operating an SPMV 120 from a client application (for example, a game client application 806 in
Towards this end, scene reconstruction translation layer may employ camera and/or servo and/or motor calibration data to determine the Euclidian location of object(s) in the scene and/or to send commands to SPMV(s) 120 and/or servo assembly 112.
According to the architecture illustrated in
In
In
In some embodiment, scene reconstruction translation layer may be associated a software or hardware component which provides ‘self-sufficiently’ and automatically calibrates the camera and/or servo assembly and/or SPMV motor. This provides yet another way to “abstract away the real world” from client hardware or application (e.g. 806 or 1806).
Thus, FIGS. 30A30 include steps S511, S515, S519, S523, 5527, S551, S555, S559, S563 and S561.
In some embodiments, two cameras 110A and 110B (or more) are provided as parts of two camera systems 92A, 92B. These two camera systems 92A and 92B (or more) may be placed on a roughly flat surface (see, for example,
In some embodiments (for example, see
In one use case (see
In some embodiments the SPMV 120 is equipped with a typically articulated robotic arm 106 controlled by the processing capability of the SPMV 120 (for example, as provided by SPMV electronics assembly 84). In other embodiments there is no such robotic arm. In yet other embodiments there may be some other moving equipment on the SPMV 120, lasers, range finders, ultrasound devices, odometers other sensors, or alternative equipment. Nevertheless, there is no requirement to include such additional sensors, and some embodiments tech operation of SPMV 120 primarily according to analysis of images of the scene including SPMV 120.
In some embodiments, SPMV 120 is designed with a generic mechanical and electronic plug whose interface has been standardized and published such that third parties can design accessories for the SPMV 120.
Although not a requirement in some embodiments, electronic circuitry 130 may include a computer unit 108 as illustrated in
As said, the setup of the system in terms of the placement of its components can be haphazard and does not require skilled or trained personnel. Once all components have been placed and plugged in or turned on, the system must now learn from its environment the exact position of its relative components and their intrinsic parameters. This is referred to as calibration of the system. Once calibration of the system has been completed, the 3D visual interface can be displayed and the SPMV guided and controlled with the precision required by the specific application.
In order to calibrate a camera, a calibration object may be employed. Some embodiments teach that the calibration object is the mobile SPMV 10 itself (for example, including onboard lights 124 or onboard mechanical shutters). This may obviate the need to introduce or manipulate a special calibration object.
In the non-limiting example of
One example of an implementation of step S917 (see
LEDs provide a very fast and computationally easy method to find a spot in the image with a known world coordinate. Assuming no movement of objects in the scene or the camera, two succeeding images one with the LED off and one with the LED on can be compared (see the discussion which accompanies
In one example, the pixels that change (i.e. which are changed IMGPOST in relative to IMGPRE) are potentially the result of the LED turning on or off. In one example, next an image is created that is composed only of the difference in intensity of the two images (assuming a gray scale image). In one example, next a convolution is performed to blur the image and add the intensity of neighbors to any given pixel. The pixel with the highest intensity may be considered the centroid of the LED flash (thus other techniques for locating a ‘centroid’ may be used). Using this method, in some embodiments, a few frames are required to locate all eight LEDs. In some embodiments, nine images are certainly enough but there is a way of allowing multiple LEDs to flash simultaneously while still recognizing each location. This may be done by each LED flashing as if spelling a binary number. Using this method, 5 frames may be sufficient. (1: none. 2: 1, 3, 5, 7 on. 3: 2, 3, 6, 7 on. 4: 4, 5, 6, 7 on. 5: 8 on) this method provides far more significant results for large numbers of LEDs. Typically, the CCU (
In order to achieve good results when detecting flashes, it may be advantageous to set the CCD camera settings to maximize the contrast between on an off. First of all, it may be advantageous for automatic gain control to be turned off. Secondly, it may be advantageous for brightness to be set at lower than the level normally desirable for human viewing. This may be done by lowering brightness to the level where the peak intensity in non-lit images are below 0.8. With this setting bright LEDs cannot be confused for other changes and neither is their reflection.
In some embodiments, it may be advantageous to mount LEDs 124 to SPMV 120 by installing them somewhat inset into the surface or flush with it in order that the reflection of the light on the surface in the immediate vicinity does not affect the calculation of the center-point of the light.
A Brief Discussion of the Robustness Properties of this Method of Using LEDs
This non-limiting ‘use-case’ system, specifically in the example just cited, relies on the comparison of two images one each of which has a different configuration of LEDs turned on or off. All the pixels in the image are approximately the same in brightness levels with the exception of the location where a LED was turned on or off where there is expected to be a large difference in brightness for a small number of pixels. This located the LED in the image. For any one image we have a number of LEDs which we can use for calibration and later for operating the SPMV.
However, this system may provide an inherent feature that it is particularly robust. It works in a large range of lighting conditions and is very fast computationally. However, it might still be the case that a lighting transition is falsely detected. For example, some other element in the scene might experience a brightness transition by some unlucky chance. Alternatively, the LED itself may be occluded (by being on the other side of the SPMV for example) but it may shine on some other part of the scene which will be recorded as if it was the location of the LED.
The solution depends on the fact that we know the relative positions of the LEDs (the distances and the angles between them). This means that if you have, say, three true LED locations and a false one, we can easily find the false one and remove it. If the camera is calibrated, this is easy. The good ones will have valid distances between them (wherever the SPMV is in the room) and the bad one will have bad distances to the other three. However, even if the camera is not calibrated we can find the “bad guy.” It can be shown that a configuration of a few LEDs that include false readings will, in general have NO intrinsic or extrinsic calibration possibility that would produce such a configuration of LED positions. This is because the configuration is constrained by the knowledge we have of the distances and angles between the real-world LEDs. Once we know that the set as a whole is bad, we can choose subsets of LED positions and see whether there is some calibration configuration for the subset. If we find good subsets, we can easily remove those that can fit into no calibration configuration.
As will be discussed below, it may be possible, in some embodiments, to employ ‘iterative techniques’ for computing one or more calibration parameter(s).
In the current example, the first task is to determine the intrinsic parameters of each camera. An initial estimate of the camera intrinsic parameters may be obtained as part of the manufacture of the system itself and not as part of the user-setup of a particular instance of the system. A simple articulation of the specification of the components is sufficient for input to the further calibration.
The intrinsic parameters are expressed by the matrix A:
α is the distance, in horizontal pixels, from the lens plane to the sensor plane (focal length if focused)
β is the same distance in vertical pixels
γ is a measure of the skew of the image
u0 is the horizontal pixel coordinate of the image center
v0 is the vertical pixel coordinate of the image center
In some embodiments of the invention, the initial pre-setup estimate of intrinsic parameters is used to calculate a more accurate set of values for the specific instance of the system.
The method used involves keeping the SPMV steady while taking a number of pictures from a single camera whose intrinsic parameters are to be determined until an image location has been measured for each LED on the SPMV. We refer to this as one data set. Next, the SPMV is instructed to turn (and perhaps move) and another data set is measured. This is repeated a number of times ranging from 4 to perhaps 100. It should be stressed that no human-operator involvement is required for this operation. The fact of powering the system in a state where its own records show that calibration has not happened yet is enough to initiate the calibration. The user need only be asked not to interfere and some form of detection of user interference should he/she not cooperate is also required for complete robustness.
The method calculates an optimal value for A such that the expected image locations of the LEDs are as close as possible to the actual positions measured. The distance from the camera to the SPMV is not known. Usually, it is not possible to determine both A and the distance to the SPMV at the same time because we cannot know whether camera magnification or distance from the object is the cause of a particular size. The reason this is not a problem can be expressed as finding the Image of the Absolute Conic or it can more simply be explained as the effect of perspective shortening. The shorter the distance to the object the stronger the perspective effect is. This need not be represented explicitly, the iteration method used implicitly uses it to achieve the correct result.
It is possible to employ the Levenberg-Marquardt Iteration technique which is well known to practitioners in the field; referred to henceforth simply as ML iteration. A good implementation can be found in [Press 2009]. It will be used extensively. In each case we will provide:
If the guess is close enough, the iteration will converge on a good value for the parameters of the model.
Assume a coordinate system as in
Writing the homogenous world coordinate vector as X and the homogenous image vector as x, we know that:
x=AR[I|−C]X (eqn. 5.2.3.1)
where I is a 3×3 Identity matrix, R is the rotation matrix of the camera relative to the chosen axis system and C is the location of the camera in that system. R can in this case be expressed as a single rotation about a unit vector. For this we need two parameters for the x and y component of the unit vector with the z coordinate implied by the unit magnitude of the vector as well as a third parameter, θ, for the rotation. C is three more parameters and A is expressed in terms of the 5 parameters given above. We already have an initial estimate for the values in matrix A and we need to generate an initial estimate for the remaining six parameters.
The first step is to generate a number of sets of data. Each set of data includes the taking a camera image and locating each of the eight LEDs in that image using the flash detection. Next the camera and/or the SPMV is moved to a different position and orientation relative to the camera and another data set is collected. For this set of data it is preferable that the SPMV is fairly close to the camera in order that the effect of perspective shortening on the further LEDs is more accentuated resulting in more accurate measurement and hence results.
We now have N sets of data each of which contain a number of world-image correspondences. For each data set, the value of matrix A is identical but R and C are different. The ML iteration is therefore required to improve the value of R and C individually for each data set and collectively improve A for all the data sets combined.
We now need to provide an initial estimate for R and C for each data set. This is done by first creating an estimate of the R and then calculating C from this value. We generate a value of R by iterating roughly through the space of possible values for R. A general 3D rotation matrix can be seen as a combination of three independent orthogonal rotations around each of the three axes. We simply divide the complete 2π rotation into 32 (or some such number) divisions. This gives 32 possibilities for the x and y axes each and another 32 for the z axis. This gives 32,768 possibilities. However, since the y axis is essentially the upwards direction, the camera system is on a roughly smooth surface and the camera is installed essentially horizontally, we can assume the z rotation is slight. 3 z rotations are sufficient for the estimate and therefore the number of possibilities to check is thus only ˜3000. A modern PC can perform the following calculation 3000 times in much less than a second typically.
For every possible value of R there is one value of C that is the closest to making a good match between world and image coordinates. We calculate this value for C and attach a score to this value of R as a sum of the square of the distance between the calculated image points and the measured image points. The value of R with the lowest score wins and becomes the input value to the ML iteration.
The following is the details of the calculation of C for a given value of R.
We write the world coordinate for any given world point, assuming a unit matrix for the intrinsic parameter matrix A, as W. It is simply calculated as:
W=A·X (eqn. 5.2.3.2)
Writing R·[I∥−C] as [R∥t], we have:
x=[R∥t]˜W (eqn. 5.2.3.3)
We write R as [R0, R1, R2] (i.e. R0 is the first row of R), x as [s·u, s·v, s] and t as [tx, ty, tz]. We use the period (·) to indicate multiplication for scalars and dot-product for vectors.
A data set includes about six valid correspondences because two LEDs are normally hidden from view. We write the value for the ith data point as X[i], for example.
s·u=R
0
·W+t
x (eqn. 5.2.3.4)
s·v=R
1
·W+t
y (eqn. 5.2.3.5)
s=R
2
·W+t
z (eqn. 5.2.3.6)
From these we get, using two data values; i,j:
t
x=((u[j]·(R0·W[i]·v[i]−R1·W[i]·u[i])−(u[i]·(R0·W[j]·v[j]−R1·W[j])))/(u[i]·v[j]−u[j]·v[i])
t
y=((v[j]·(R0·W[i]·v[i]−R1·W[i])−u[i])−(v[i]·(R0·W[j]·v[j]−R1·W[j]·u[j])))/(u[i]·v[j]−u[j]·v[i])
t
z=((R0·W[i]+tx)/u[i])−(R2·W[i]) (eqns. 5.2.3.7)
Different results are obtained for every pair of values chosen from the data set, so a few pairs are chosen (1&7, 2&6, 3&5) and the values are averaged. Once a value is obtained for t, the score is calculated by applying [R|t] to every value in the data set obtaining a set of image points that would be where the LEDs would be imaged if the chosen value of R was the correct one. Distances are measured from the calculated values to the real value and the sum of the squares of these distances is the score given to R.
We iterate through the ˜3000 values of R and find the R with the lowest score. This value along with the calculated value for the camera center C becomes the estimate that is input into the ML iteration for that data set.
Next we perform ML iteration on this one data set in order to improve the values for R and C. A, the intrinsic parameters of the camera is still held constant. The model for ML used is as follows:
P=A[R|t] (eqn. 5.2.3.8)
such that P is the camera matrix. We apply P to each of the world coordinates of the LEDs and thus determine the calculated image points. The error function is the sum of the squares of the pixel distances between these and the actual image locations of the LEDs. Thus we get in improved value for R and t (and hence C) for the data set. This is the value that is used for the next stage.
Next, we combine a number (for example, between 4 and 100) of data sets. Each data set already has an optimal value of R and C of its own. However, they all share a value for A. We now input all the data sets into an ML iteration where the model is exactly the same as the last one. The model is eqn. 5.2.3.8 and the error function is the sum of the squares of the distances between points calculated using PX and the measured locations as before. The only differences are that, we now have multiple data sets each with their own R and C and that all the parameters A, R and C are allowed to change. The only restriction is that while R and C are applied only to the values of their own data sets, all the data sets must have the same A.
As a result we now have an optimal value of A. The same procedure can be applied to each of the cameras since their intrinsic parameters may be different.
In some embodiments, the data sets should be measured with widely differing orientation of the SPMV. The plane (more or less) formed by the top of the SPMV does not change, which is a problem. However, the plane (more or less) formed by the front, back and sides change significantly thus ensuring a robust value for A.
The model we just described will be referred to as the fixed camera center camera matrix model. The process of calculating a seed value for this model will be referred to as the fixed camera center seed determination process and the process for calculating A from this model will be referred to as the Iterative ML fixed camera center process for calibration of camera intrinsic parameters.
We now have a value for A. This need not be determined too often. It should be performed on the initial setup and then infrequently. If the lens on the camera can be adjusted manually, this will change the value of A. Such a change should be discouraged and if it is done, the system should be “informed” in order to do the calculation again.
Next we seek to determine the calibration, position and orientation of the camera-system in some world coordinate system. Our goal is to develop a model for the extrinsic (R and C) parameters of the cameras as well as the parameters of the servos that pan and tilt the camera. We need to know all the fixed parameters of the model and their relationship to command parameters from the controlling computer.
Again we start with the coordinate system as in
θ=khih+θ0 (eqn. 5.2.4.1)
φ=kviv+φ0 (eqn. 5.2.4.2)
We write O as [Ox, Oy, Oz] and C, the camera center as [Cx, Cy, Cz]
C
y
=L cos(φ)+Oy (eqn. 5.2.4.3)
C
x
=L sin(φ)sin(θ)+Ox (eqn. 5.2.4.4)
C
Z
=L sin(φ)cos(θ)+Oz (eqn. 5.2.4.5)
The extrinsic camera matrix requires a rotation matrix. The model as described so far already specifies the camera center, C. We also have three rotation angles, but they are not orthogonal so a slightly different approach must be used to calculating R.
The vertical servo which rotates the camera about the x axis can be treated as a standard matrix of the form of a Givens Matrix which we'll call Qx:
Thinking simply, the matrix for horizontal rotation can also be a Givens matrix of the form:
If we calculate the horizontal rotation first, the axis of vertical rotation physically moves with the camera. However, in the new coordinate system (calculated after the horizontal rotation alone) the axis of vertical rotation is actually still the x axis of the new system. Therefore by calculating the vertical rotation second, a Givens matrix can be used too.
We now consider performing the same calculation with vertical axis first. We do this in order to introduce a method for calculating an error due to the fact that the camera may not be situated exactly horizontally but may be skewed a little as represented by a small rotation about its own z axis.
We first calculate the vertical rotation using the vertical Givens matrix above (eqn. 5.2.4.7). However, now the horizontal rotation about the y axis cannot take the simple form of eqn. 5.2.4.6 because in the new coordinate system the world y axis about which the horizontal servo rotates has been rotated too and is now in a new position in the y-z plane. So instead we use the form for a rotation about a unit vector. We use the following routine (defined in C) to generate a rotation matrix expressing a rotation of theta about a unit vector [x, y, z]:
. . . where DP is a double precision typedef. Mat_DP is a typedef that defines a two-dimensional matrix.
We start off with the y axis vector [0, 1, 0] and apply Qx (eqn. 5.2.4.7) to that thus generating y′, the axis for the now not-so-horizontal rotation. We then use the above method with the unit axis y′ to generate the second rotation matrix. This method gives identical results to the first method, the difference between the two being simply that the order of matrices has been reversed and therefore in the second method the effect of the first on the second's axis of rotation must be accounted for.
The z axis rotation can be accounted for similarly. Assume that the camera is rotated a fixed angle about its own z axis, ω. If we calculate the z rotation first, the axes of vertical and horizontal rotations will now have to be modified by the z rotation. We can thus use either order of calculating the next two rotations. If we do the vertical first, the x axis is first modified by the z rotation to create a new unit axis x′ and then the unit-vector routine given above can be used to generate the “vertical” rotation. Next, the combined rotation matrices can be applied to the y axis to generate y″ and the unit-vector routine used yet again to supply the third matrix.
Since the model can now calculate R and C, together with A, we now have a complete model of the camera imaging system including a camera matrix (the basic conversion of world coordinates to image coordinates as given by eqn. 5.2.3.1) in terms of α, γ, u0, β, v0, θkv, iv, θ0, φ, kv, iv, φ0, O, C, L and ω.
These parameters change at different times. Assuming that the lens cannot be adjusted, that the length of the arm of the vertical servo is not significantly temperature dependent, that the seating of the camera on the system is not adjusted and that the servos do not start to wear out, α, γ, u0, β, v0, kh, kv, L and ω will stay the same all the time. It might be good to calibrate them every now and then but we can refer to these as fixed for a particular instance of the system. Thus an initial calibration is required to ascertain these values and then they can be left unchanged. (The only reason to change these later on is if our system is continuously providing readings which might improve the accuracy of these values.) We refer to this set as the system-constant model values.
Whenever the camera system as a whole, including the base, is moved or turned θ0,φ0 and O will change. Thus whenever we determine that the camera base has moved, these values should be recalculated. These can be figured out so quickly that it makes sense to do this often, particularly at start-up or perhaps at the start of each new assignment. We refer to these as the position-constant model values.
ih and iv, are under direct control of the software running on the controlling computer and consequently C, θ, and φ change whenever they do. This is the basic action of moving the servos and therefore redirecting the cameras. We refer to these as the controlled model values. The purpose of creating this model has been to determine the values of all the other parameters in the model and therefore to generate the controlled models values and subsequently the Camera Matrix (P=AR[|−C]). If we know all the parameters of the model then every time the controlling computer sends a new servo position instruction we can calculate the complete Camera Matrix including both extrinsic and intrinsic parameters.
The model we just described will be referred to as the pan-and-tilt camera matrix model.
We use this model in three phases:
6. Seed the initial values α, γ, u0, β, v0, θkh, ih, θ0, φ, kv, iv, φ0, O, C, L and ω using one of the processes described below. However, even if we know values for the system-constant model values, we need a way to provide initial seed values for the position-constant model values. This can be achieved by inputting the first data set collected in step 2 and using these as input for a fixed camera center seed determination process. This gives us a value for θ, φ and C. For the seed value we set O as being L below C. We then calculate θ0 and φ0 using manufacturing values for kh and kv and using the equations for θ and φ given above. We now have all the constant values we need as seeds from the model from the first data sets. For the first as well as all the data sets we now use the values of ih and iv from step 3 and the seed values of all the other model values to generate seed values of θ and φ.
In order to seed the model we can use one of the following options:
The SPMV is moved by motors, some of which may contain internal feedback mechanisms which allow them to be set to turn to a specific angle. Such motors may have speed and power controls. The motors will, in general, turn the SPMV or one of its components, or move the SPMV or one of its components. The motors will take some form of input ranging from turn off and on, analog or digital signal input levels, period or frequency in the input signal, duration of input or some such variation that determines the speed, power, angle or some other output parameter.
In general, particularly for cheaper components, the mapping from input parameter to output parameter is not exact. Firstly, each instance of the motor might have a different mapping. Secondly, over time the mapping may change. Motors may wear down or battery charge levels may change resulting in different motors performance. Thirdly, the mapping may depend on such simple differences as the material of the floor or work surface. The SPMV may move much more slowly on carpet as on marble.
For these reasons it is important to create calibration for the mapping of motor inputs on the SPMV to motor outputs. This is done by providing a range of inputs to the motors and for each input monitoring the motor response. A simple example may be sending an “on” signal for a variety of durations and in each case determining the resultant change in the position of the SPMV. The positions of the SPMV before and after the command can easily be determined using all the techniques of location determination using LEDs described here. The result of these measurements is a mapping between a set of inputs and a set of outputs. These can be put into a table and used by “looking up” the desired result and finding the closest appropriate input. Alternately, a mathematical model can be built using standard numeric techniques such that a simple linear or non-linear function relating input to output is described and the constants of that function are determined by the observations.
While using servo positions to both calibrate a system and to provide triangulation information, a problem arises. A servo is controlled by the pulse width of the signal sent to it by its controller. The value of i (ih and iv) in the previous discussion ultimately translates to a specific period for which the signal is high out of a repetition period of approximately 2 ms. However, servos are built with a deadband which is a range of values within which the pulse width may change without causing any change in position in the servo. The reason for this is to prevent “jittering” as the servo responds to minor noise in the controlling signal.
This may present a problem for our mechanism because it means that the servo position is not an exactly repetitive result of the input i. In practice, if multiple images are taken with the same input value i, each image may differ from the others by a small number of pixels. In one specific configuration this was up to 4 pixels at most. However, higher resolution cameras will mean that this number may be larger. The market for servos includes both “analog” servos with a large deadband and “digital” servos for which the deadband can be controlled. Therefore, for more expensive implementation of this invention this error can be reduced. However, in such cases the microcontroller must guarantee a very exact pulse width requiring fast, dedicated microcontrollers.
We now present a method for handling this servo inaccuracy. This can be applied for the calibration phases as well as for later SPMV guidance phases as will be explained below.
We refer to the input value for the servo position as i. However, we can deal with the deadband issue as if the servo actually received a somewhat different value of i, ia. This is the exact value the servo actually uses to set its position. If a small change is made in the value of i and the change is within the deadband region and the servo does not move at all we can say that ia has not changed at all. As we continue to increase i, eventually the servo will move a significant amount that is not proportional to the last change in i. We can now say that the value of ia has “jumped” to a new value proportional to the actual move in the servo. In calibration phases as well as the later SPMV guidance phases we must use the value ia instead of i in our calculations.
When a change is made in ia, a rotation is effected and the image changes by a specific number of pixels approximately proportional to the change in ia. The number of pixels moved can be counted in principle thus giving a way of figuring out ia. In practice, except for very small changes in ia close to the principle axis of the camera this relationship is not linear. It is normally a rotation in the image coordinates x and y and it “stretches” as it moves further from the principle axis. We must account for this non-linearity.
The first method applies to the calibration phase. During the calibration phase this can be accounted for simply by allowing the given value of ih and iv attached to each data set to change with the ML iterations. This is unusual because these values were data values till now and not model values. Therefore, they must be copied to the model values before the iteration phase. However, a restriction must be applied. The average values of all values of i thus modified must remain the same. Otherwise, the ML engine could simply change arbitrarily in order to minimize the error function. By making this change in the ML model, we can account for the deadband effect during calibration.
A second method for correcting for deadband is during regular camera operation. Later, when the system-constant model values and the position-constant model values have been determined, whenever the camera is moved, instead of applying the new value of i, we can test the change. We use our model to recalculate what we expect the new image to look like. If we had the exact value of ia we would be correct except for the part of the image that has just moved into view. For the purposes of this calculation we ignore the part of the image that has just come onto view and focus on the remainder: the part of the image that is identical in both images but has been rotated due to the camera movement.
Starting with i as the first guess for ia, we calculate the 3×3 Homography matrix, H, that would convert the first image into what the camera would see if it was rotated by a servo with input ia and call this the calculated image. We know from our calibration how to calculate R, the rotation matrix that the change in ia applies to the servo. H is given by
H=ARA−1 (relevant for step S527 of FIG. 30A and for FIG. 30B)
Where A is the intrinsic camera matrix we have been using all along. Actually this is true only where the rotation is about the camera center. In reality the center of rotation is not about the camera center but for small movements of the center relative to the distance from the points of interest this is often good enough. If it is not good enough, a more accurate equation for H can be used which relies on the fact that most of the points in the image are on a known plane. In our case, that is the plane of the floor or work surface which we have defines as the plane y=0. The general equation for H, where the center of rotation is not the center of the camera is given by a definition of the plane in a coordinate axis of one of the cameras (R must also be converted to that coordinate frame) as π=(nT, d)T as
H=A(R−tnT/d)A−1 (relevant for step S527 of FIG. 30A and for FIG. 30B)
Once we have expected or calculated image and the actual image after rotation we perform a Gaussian convolution causing a slight blurring on both followed by a Sum of Absolute Difference (SAD) between the pixels of the calculated image and the actual image. If the result of the SAD is close to zero we can conclude that indeed ia=i. However, if it is not, we iterate through minor changes in ia in both directions. For each iteration we calculate H, generate the expected image, blur it and perform the SAD. We then select the iteration with the lowest sum to be the actual value for ia.
This mechanism produces a very good value of ia but it assumes nothing in the actual scene has changed; only the camera has moved. If the camera is tracking a moving SPMV, this may not be the case. There are a number of potential responses to this issue.
Once calibration of system-constant model values and position-constant model values has been completed, the instance is ready to guide a SPMV. Movement commands are sent to the SPMV and, in transit, an LED data set is recorded. This can now easily be used to calculate the position of the SPMV. Since the camera matrix, P, is known we can write for each LED:
x=PX (eqn. 5.4.1.1)
where x is the homogenous vector of the image of the LED and X the homogenous vector of the world coordinate of LED. We define the pseudo-inverse of the camera matrix, P+ as
P
+
=P
T(PPT)−1 (eqn. 5.4.1.2)
The theory and calculation of pseudo-inverse is well-known in the field.
Define a vector V that joins the center of the camera, C to the point P+x. Thus:
V=C−P
+
x (eqn. 5.4.1.3)
We define a line of points in world space as:
Pt=P
+
x+λV (eqn. 5.4.1.4)
where λ is an arbitrary constant. We know that any point Pt will be imaged at the point x. We can expand eqn. 5.4.1.4 into its scalar components:
Pt
x
=P
+
x
x
+λV
x
Pt
y
=P
+
x
y
+λV
y
Pt
z
=P
+
x
z
+λV
z
However, since we assume that the SPMV is still on the same surface as that we defined as y=0, we know the Pty coordinate of the LED since wherever the SPMV moves (assuming it is either wheeled or can move its legs into a “rest” position) the Pty coordinate of the LED will remain the same. If we know the Pty coordinate of Pt and we know the other elements of eqn. 5.4.1.4, we can easily calculate A. Once we know λ, we can use eqn. 5.4.1.4 to calculate the Ptx and Ptx coordinates of the LED. In theory, two LEDs are sufficient to give the position and orientation of a simple wheeled SPMV since the full world coordinates for any one LED can be calculated fully and independently using this method. Collecting a full data set allows Least Squares method to be used to increase the accuracy. However, if speed is important to generate real-time guidance information, only two LED locations need be measured. Speed is necessary if the method is being used to correct the SPMV that has veered slightly off course and accurate arrival is important. Guidance may also be necessary if for reason of cost the mobility mechanism of the SPMV is very inaccurate.
This method calculates L using the known Pty value and then generates Ptx and Ptz values. However, this works well for a wheeled vehicle or some other SPMV whose motion type is such that we can know the height of the LED above the floor. For other SPMV kinds, such as those that use legs and therefore the Pty coordinate is not known, the location can still be calculated quickly. All that is necessary is to determine more LED pixel locations. We thus have more than one instance of eqn. 5.4.1.4. with different values of λ: λ1, λ2 . . . . Since we know the relative distances and orientation between the points, we can provide these as constraints on eqn. 5.4.1.4 and thus trivial algebra will provide the real-world location of the LEDs. This method is almost as fast as the method using known height above the floor (work surface) and is still easily fast enough for real-time determination of real-world location using a single camera.
The entire guidance calculation can be performed in well below 1/10th of a second with standard cheap cameras and a low-end controller at the camera. This makes this method extremely significant for real-time interaction. The down side of this method is that it applies only to the SPMV or an object outfitted with LEDs at accurately known location. As will be explained later, this method that provides very high speed at low cost can be combined with other methods in order to interact effectively with the environment. This combination is a key teaching of this invention.
When the SPMV approaches the edge of the camera image, the camera must move in the general direction where the SPMV is about to exit the image. At this point i, the input signal to the camera servos will change and the camera model may be used to calculate a new camera matrix. Depending on the constraints of the task, image matching may be used to increase the accuracy of ia as described earlier.
We have thus far described the single-camera calibration of the camera and servo mechanism as well as the mechanism for fast location and guidance of a SPMV. We now start the discussion of two-camera system that can provide both an internal and an external representation of the 3D geometry of the whole system. This latter mechanism must be integrated with the former single camera mechanism in order to achieve best results.
A Brief Discussion of LEDs on Objects Other than SPMVs
LEDs as described here provide a powerful mechanism for real-time detection of position. It should be noted that this method can also be used to speed detection of other objects besides an SPMV. For example, the goal posts of a game could have LEDs in them to facilitate exact location. In another example, a LED could be embedded in a ball used for Soccer. The significance of this second example is that the object may be moving fast and requires very fast response time.
In some embodiments, a plurality of camera are used (see
The literature contains a vast amount of methodology regarding scene reconstruction using a stereo rig of cameras. There is no point repeating it here. This document will focus on the implementation details that are specific to this invention. These are as follows:
The first challenge presented by an independent pair of cameras is that they need to point to roughly the same area. For depth information to be extracted from the pair of images, the same scene components must appear in the same image.
One solution for this is to combine the images from multiple positions of the same camera into one very large image and apply stereo matching algorithms to a pair of combined images. The method for combining images for a panning or rotating camera is widely discussed in the literature. There are, however, further challenges that arise by using this method.
Here we describe a different solution to this problem. This solution takes advantage of the fact that the camera position and orientation is known to a reasonable level of accuracy. Assume that most points of interest in the scene are on or close to the floor (or the working surface) which defines the y=0 plane. Both cameras can point their principle axis onto the same point on this plane. Clearly some points will appear in one image and not in the other, but around the center of the image, many points will appear in both. Once other planes are detected as described above, the same method may be applied using the walls of the room as reference planes.
The next challenge is that the two cameras will not be, in general, aligned horizontally. Many stereo matching algorithms described in the literature assume that the camera centers of the two cameras can be described as a horizontal translation in terms of the images, with no rotation. In this mechanism, the matching epilines, the lines on which image correspondences will occur, are simply the horizontal lines of the image of the same image y coordinate. In our case, we need to extract the matching epilines from our knowledge of the camera positions and orientation. This will now be described.
All the epilines meet at the epipole. The epipole for first camera is the image of the second camera center in the first image. Since we know the camera matrix of the first camera and the world coordinates of the second camera center we can calculate the epipole for the first image.
We now generate epilines radiating from the epipole in 360°. The number of such lines depends on the resolution we want to work at and the processing power available. Next we find which lines will cross the actual image. For example, a typical image may have pixel coordinates from 0 to 320 horizontally and 0 to 240 vertically. A typical epipole may be at coordinate −1500, 150 which is way off on the left and side beyond the actual image. We calculate which of the epilines cross into the actual image and discard those that do not cross two of the image borders. We are now left with a set of potential epilines from the first image.
We write the camera matrices as P1 and P2 for the first and second cameras respectively, and similarly C1 and C2 for the two camera centers, e1 and e2 for the two epipoles, I1 and I2 for specific matching epilines. We write F, the fundamental matrix (actually the essential matrix since the cameras are calibrated) as:
F=e
2
skew
·P
2
·P
1
+ (eqn. 5.5.2.1)
where eskew is the skew matrix formed from the 3-vector e.
As described in standard stereo matching literature, if an epiline is defined by a vector I1 written as [a, b, c] where ax+by+c=0 is the equation of the line for the epiline, the matching epiline is calculated as:
I
2
=F·e
1
skew
·I
1 (eqn. 5.5.2.2)
For all the epilines in the first image that cross the boundaries of the first image, not all matching epilines in the second image cross the boundaries of the second image. In that case both the epilines that do not intersect the second image and the corresponding epilines that generated them must be discarded.
We now have a set of matching epilines from the two images. We can create two new images using them. We will call these epimatch images. The image is formed by creating a fixed interval of the radius from the epipole of each image depending on the resolution and accuracy we want to use for the stereo matching. All the points on the epiline generated using this interval that fall inside the image are used to generate the epimatch image. If we are using a gray-scale image we can, for example, generate the epimatch image pixel by performing a simple bilinear filtering on the four nearest neighbor pixels of the original image. (The generated point on the epiline does not, in general, have integer x-y coordinates.)
The two epimatch images are now of the form used in standard stereo matching literature where each horizontal line of the first image is a corresponding epiline of the equivalent horizontal line of the second image. Standard techniques for matching the features of the two epilines can be used and need not be discussed here. We will however, discuss some fast techniques that take advantage of the integration of the stereo-based information with the single-camera knowledge that we have already gained.
It is important to note that the two epimatch images that have been generated should still retain the original coordinates of the images from which they were generated. This can be achieved simply using a parallel data structure. Once we know which pixel on one epiline matches the pixel of the other epiline, we will use the original coordinates in order to produce the triangulation that gives us the world coordinates of the scene point that generated the two corresponding image points.
It is possible to improve the fundamental matrix F using ML iteration at this stage. If we do not assume that a match must occur only on the actual matching epilines but allow a leeway for finding matches, say on up to 4 nearby epilines we can find and record corresponding points using this relaxed assumption. Any technique may be used to find this new set of corresponding points. Great success was achieved using a simple box matching algorithm. We now have a data set of thousands of point correspondences that should all obey the model:
x
2
·F·x
1=0
If we now create a simple error expression using:
Error=|x2·F·x1|/|F|
we have all we need for an ML iteration. We can allow the values that comprise F to change in the iteration in order to minimize the error function.
However, this is not a good solution because we move away from the information we have already calculated regarding the position and orientation of the cameras. Our goal is not to change this information but just to get a value for F that maximizes our ability to find matches along the epilines. We therefore can perform the ML iteration and also include data sets from the LED matches or other world-image correspondences that we know. Alternatively we can add to the error function a term that penalizes moving F away from the parameters of the camera matrices underlying it. Another solution is simply to form F′ such that F′=HF where H is some homologous transformation 3×3 matrix. We then allow only H to vary in the ML iteration.
Stereo matching algorithms are notoriously slow. This is one reason that we require combining the fast single-camera process for determining the location of a moving SPMV as will be described later.
Identifying Extrusions from a Surface
We now describe a fast method for extracting important scene information from a stereo rig calibrated according to the methods taught here.
Assume all the points in the image are actually part of the floor (or work surface) and thus at world coordinate y=0. Form the epimatch images as described. Next find the x and z world coordinates that would generate the image point if the world y coordinate was 0 using the method described earlier for fast calculation of LED position in section 5.4. Next create correspondences along the epilines ignoring gray-scale or color discrepancies between “corresponding” points but simply using the x and z coordinates calculated for each point along the epiline.
Now perform a slight Gaussian blurring and calculate the SAD of the “corresponding” pixel and perhaps that of a 3×3 box around the pixel. If the value of the SAD is low then we can conclude that the assumption for this pixel was correct and indeed it is part of the floor. However, if the value of the SAD is high, then the assumption is incorrect. We take the “image” of the SAD values and convolve with a larger Gaussian to create a bigger blur in order to accumulate the SAD values of neighbors. We can say that wherever we have a big area of high SAD values, there is some rising from the floor probably corresponding to an object placed on the floor or an obstacle.
This method is fast and produces good results. Furthermore it is a good method to use as a starting point for finding correspondences on the epilines. Normally the problem starts off as an O(n2) problem where n is the number of points in the epiline because initially any point may match any other point. However, once most of the points have been determined to be floor points, there is only a need to focus on the remaining points and thus n is drastically reduced.
The method can also be applied to planes in the scene that are not part of the floor. Once other planes are detected (vertical ones are normally expected), the same method can be applied to find these. Thus the walls of the room can also be quickly determined.
We now describe another method for identifying objects within the scene. The SPMV can be guided to move up to a specific extrusion or some other area that has potential for being a separate object. The SPMV is then guided to apply some force to the object. If the object is relatively rigid and it moves, its movement will have a uniform rigid nature. Motion estimation applied to the images before and after the movement can be analyzed for this kind of rigidity. All pixels participating in the movement may be marked as being part of the object.
The stereo camera system can be used to generate a complete 3D representation of the scene of interest. Once the multiple stages of calibration have completed, if the system is allowed some time, the cameras can start turning on their own accord and produce stereo images of the entire range of movement of their servos. The methods just described as well as standard stereo scene reconstitution methods are applied to these images. The results are all combined into one internal representation of the entire room. This may take a long time. However, the result is very impressive. As will now be described, not only does the system now have a “knowledge” of the entire room and all its non-moving components, but now we can display to the user the room he is in using full 3D display technology. Over time this internal representation can be updated if the “motion” that has been detected is determined to be non-transitory.
Having such an internal database of the 3D features of the room and the images captured at different angles also speeds up processing of moving components in the room. When new images are taken, they may be compared to the initial database thus yielding immediately which area of the picture has changed. Stereo recreation of the new object or motion can focus on this area alone and thus reduce the O(n2) problem described earlier.
Once we have an entire representation of the scene of interest, this actually takes the form of what is known in the literature as a “point cloud”. We have millions of points in the room whose 3D coordinates we know. These are all on the surface that is present to the cameras (ignoring transparent objects). We now want to turn this point cloud into a surface which may be partially planar and partially a very complex surface. The immediate use is for a visual representation. However, there are other uses including object recognition.
The method used to turn the point cloud into a surface is called tessellation. Methods for tessellation are described extensively in the literature and need not be expanded upon here. The only point specific to the methods described here is the starting point of tessellation. Tesselation works well when it starts with a 2D grid of points (each of which has 3 world coordinates: x-y-z). The 2D grid coordinates may be arbitrary as long as they make tessellation easy. The 2D grid chosen is the grid of pixels in the first epimatch image described above. The epimatch image specifies whether a pixel has a corresponding pixel in the other epimatch image and therefore that this pixel is a valid point on the grid.
We therefore have a 2D grid with some elements of the grid invalid and the remaining elements representing a pair of corresponding image points and therefore a valid world coordinate from the pixel cloud. The first task of the tessellation is to create triangles that are formed from points of the grid as close to each other (on the grid) as possible while avoiding the invalid elements.
The basic algorithm for tessellation works with pairs of grid lines generating basically optimal triangles as it goes along the lines. We try to generate pairs of triangles each time, one bottom-left and the other top-right. The disturbing factor is that an invalid grid element cannot generate the vertex of a triangle. In the simplest case we start with four adjacent valid elements two along the top and two on the bottom which generate two simple triangles.
In other cases, the there may be invalid elements on the bottom or top and the algorithm has to deal with these accordingly. We provide the C source code for the implementation of a tessellation algorithm. The code is commented sufficiently to understand the algorithm. The only detail that one needs to know about the structures provided is that STesselEl holds the data for each element in the tessel grid and that it includes a member bWCValid that has value true if the grid element has valid world coordinates associated with it and is thus valid for use as a vertex in a triangle of the tessellation process.
The method as described provides one way to produce a triangle mesh from the point cloud calculated in the previous sections. The triangle mesh can now be used with any standard 3D rendering engine such as DirectX or OpenGL in order to present to a user a 3D representation of the screen. Such engines provide the ability for the user to move a virtual view-camera around the scene and inspect it from different angles. This provides a very intuitive and useful way to interact with the scene for both programmers and users. It is possible to zoom in on an item of interest or inspect from a different angle.
A triangle mesh by itself does not provide all the information that the 3D model can present. The way to create an impressive information-laden image is to apply the images from the camera as a texture for the mesh. This is easily done given the methods described so far. The point cloud that was used in the tessellation process includes a 3D world coordinate associated with the point but also the original pixel coordinates of each of the camera images used in the triangulation. These images can be applied as textures with the texture coordinate associated with each vertex in the mesh being the original pixel coordinates. There are two textures whereas a simple texturing process requires but one. Some implementations may choose to use only one of the textures. Other implementations may choose to apply both textures with an alpha value of 0.5 applied to each. Advanced 3D graphics engine support such multiple texture application.
Of course, with only two cameras, any scene components hidden from either of the two images will not be displayed in the internal representation or in the graphic display. This can be resolved by adding more cameras at different angles on the scene. Thus, some implementations will choose to utilize any number of cameras using similar methods to those taught by this invention.
There are various methods for combining triangulation from more than two cameras. The literature includes mathematical treatments of n-camera systems where n>2. Other implementations may choose to stay with the 2-camera system as described. Each pair of camera triangulates coordinates as described. There will therefore be some points that are calculated multiple times with slightly different coordinates in each case (usually). In that case a simple weighting system can be used to integrate the different results.
Incidentally, multiple camera systems can also be used to cover more complex scene geometries. For example a corridor with corners may be represented using a number of cameras such that each part of the corridor is covered by at least two cameras (where coverage includes the ability to turn the pan-and-tilt system towards the point of interest). In that case the system as described can be used both to get better all-round information as well as to allow camera hand-off as the area of interest is progressed along the corridor.
The image produced by the 3D graphic engine using this method is satisfactory but much image detail is lost. The reason for this is that since the triangles are small and the surface has a large degree of fluctuation (due to calculation and observation error factors) relative to the size of the triangle, the normals of the triangles vary quite strongly. This causes the texture of the individual triangles to be viewed at angles that distort the detail of the overall texture.
There are a number of solutions which will be now presented to this issue that provide rather excellent results:
Using any of these methods produces an excellent image with most of the scene showing at the resolution of the original image.
Method 1 above, however, will be used in any case to analyze the significant planes in the scene. Any areas of the mesh that are significantly different from the nearby plane are considered as an object in their own right for the purposes of the internal representation.
Interacting with the Visualization
When a user clicks on any point in the 3D graphic representation of the scene, advanced engines are capable of providing the nearest vertex to the 3D representation of the location of the mouse click. This feature may be used to allow the user to interact with the scene details. The click is used to make a selection which may be interpreted as follows:
We now describe a programming environment which is designed to allow programmers to create applications for the system described in this document. An application created using the programming environment is software that is written once but runs on many instances of the system. At any one time, a specific application is running on an instance of the system. When we refer to the programming or development of an application we mean the application as a class. When we refer to the use or operation of an application we mean a specific instance of the application. The purpose of an application is to specify the behavior of a robot as a function of:
The system as described frees the programmer from any vision analysis, triangulation or such like. The program can be written simply in terms of the world coordinates as described in the document till now. This can take two forms. It can be specified in absolute coordinates or in coordinates relative to a specific object within the scene. Thus an application can receive coordinate for a robot and take action accordingly. This puts programming of the application within the area of expertise of a large pool of programmers as opposed to the very specific specialization of say, stereo scene geometry. The application is written in terms of objects referred to by labels. Thus in the example given before, a programmer may write an application in terms of “goal posts”. The application may choose not to specify what or where the “goal post” object actually is. There are two choices of implementation. In one, the system can provide the application with the geometry and coordinates of all objects of the scene together with the image of them and the application will apply object recognition techniques and context to specify that a given object is a goal post. Alternatively, the application may ask the user to make a selection using the selection-interaction system described in section 5.7 in order to specify to the application which object should be the “goal post”. Either way, the goal post ends up as a specific object with a location in the application instance and the program can execute instructions relative to the object/location.
In the hierarchy, system-application-instance, application does not mean all the software. The system itself includes a layer of software and the application is a layer of software on top of the system software. In the preferred implementation, the system layer of software runs on the controlling computer and comprises the entirety of the firmware on the camera system and the robot.
We now describe one possible method of interaction between the application and system software.
The system software runs the user interface application. It therefore controls the screen resources. The system as seen by a user on the controlling computer is a 3D graphic environment. The standard method of writing applications where the application controls menu items, buttons etc. is not applicable in this case. The application software may run as a separate executable, a dynamic link library (DLL) or as a script compiled or interpreted by the system software. If it is a separate executable it must establish communication mechanisms with the system software. If it is a DLL, it provides call-back functions for the system to call. In any of these three cases, it will not control any screen resources directly. Nevertheless the application designer requires the user to interact with the system in a way specific to the application.
The method proposed for doing this is that the application provides artificial objects for inclusion in the 3D graphic environment. For example, the application may provide the triangle mesh and texture for a red 3D cube that when pressed ultimately calls an application function. The application function may change the color of the cube to green to provide feedback to the user of the interaction and simultaneously launch a routine to, say, move the robot to the first base. The system software renders the cube in the scene as if it was part of the room. These artificial objects will be referred to as interaction objects. The application can chose to place the interaction objects in the scene in one of two ways:
The application is informed regarding any change in the scene that the system is aware of. This would definitely include changes in the robot position. It may also include other real-time movement of objects in the scene. The system uses motion estimation on the background of a static image to recognize when a small area of the image has changed. In that case triangulation information for a small area may be achieved at speeds that are good enough for real time. The application is informed of such changes. The most significant change of this type is when an object that has a label attached is moved. In such cases the application would require the system to recognize that the object in the new position is the same as the one in the old position.
It is further noted that any of the embodiments described above may further include receiving, sending or storing instructions and/or data that implement the operations described above in conjunction with the figures upon a computer readable medium. Generally speaking, a computer readable medium may include storage media or memory media such as magnetic or flash or optical media, e.g. disk or CD-ROM, volatile or non-volatile media such as RAM, ROM, etc. as well as transmission media or signals such as electrical, electromagnetic or digital signals conveyed via a communication medium such as network and/or wireless links.
Having thus described the foregoing exemplary embodiments it will be apparent to those skilled in the art that various equivalents, alterations, modifications, and improvements thereof are possible without departing from the scope and spirit of the claims as hereafter recited. In particular, different embodiments may include combinations of features other than those described herein. Accordingly, the claims are not limited to the foregoing discussion.
This patent application claims the benefit of U.S. Provisional Patent Application No. 61/204,905, filed Jan. 13, 2009 and the benefit of U.S. Provisional Patent Application No. 61/241,914, filed Sep. 13, 2009.
| Number | Date | Country | |
|---|---|---|---|
| 61204905 | Jan 2009 | US | |
| 61241914 | Sep 2009 | US |
