Patent Grant
12175741
Aspects of embodiments of the present disclosure relate to soft robotics, and in particular, a soft robot gripper that is configured to deform using guidance of a vision system.
Advances to the field of soft robotics have allowed the use of soft robots to grasp a larger variety of objects than what is possible with traditional robots that have rigid end effectors. For example, soft robots are generally equipped with end effectors that are flexible and soft, to allow the robots to gently grab and manipulate delicate or irregularly shaped objects. Despite the advances in soft robotics however, challenges remain. For example, it may be challenging for soft robots to pick an item from a bin that is cluttered with other items.
The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure, and therefore, it may contain information that does not form prior art.
Embodiments of the present disclosure are directed to a computer-implemented method for picking an object from a plurality of objects. An image of a scene containing the plurality of objects is obtained, and a segmentation map is generated for the objects in the scene. The shapes of the objects are determined based on the segmentation map. An end effector is adjusted in response to determining the shapes of the objects. The adjusting the end effector includes shaping the end effector according to at least one of the shapes of the objects. The plurality of objects is approached in response to the shaping of the end effector, and one of the plurality of objects is picked with the end effector.
According to one embodiment, the shaping of the end effector includes moving a portion of the end effector from a first state to a second state, wherein the first state is an equilibrium state, and the second state is a non-equilibrium state. In the second state, the portion of the end effector may retract by an amount determined by the one of the shapes.
According to one embodiment, the shaping of the end effector includes: predicting a shape of the end effector configured to provide an optimal grasp of the one of the plurality of objects, wherein the shaping of the end effector is based on the predicting of the shape.
According to one embodiment, the end effector is at least one of a pin, tube, or suction cup.
According to one embodiment, the one of the shapes is the shape of the one of the plurality of objects, and the method further comprises: identifying a grasp point on the one of the plurality of objects, wherein the shaping of the end effector is based on the identifying of the grasp point.
According to one embodiment, the method further comprises: in response to approaching the plurality of objects, re-shaping the end effector based on determining a second shape.
According to one embodiment, the method further comprises: determining poses of the objects in the scene, wherein the determining of the shapes is based on the determining of the poses.
Embodiments of the present disclosure are also directed to a system for picking an object from a plurality of objects. The system comprises one or more cameras for obtaining an image of a scene containing the plurality of objects, and a processing system coupled to the polarization camera. The processing system comprises a processor and memory storing instructions that, when executed by the processor, cause the processor to perform: generating a segmentation map for the objects in the scene; determining shapes of the objects based on the segmentation map; adjusting an end effector in response to determining the shapes of the objects, wherein the adjusting the end effector includes shaping the end effector according to at least one of the shapes of the objects; approaching the plurality of objects in response to the shaping of the end effector; and picking one of the plurality of objects with the end effector.
These and other features, aspects and advantages of the embodiments of the present disclosure will be more fully understood when considered with respect to the following detailed description, appended claims, and accompanying drawings. Of course, the actual scope of the invention is defined by the appended claims.
Non-limiting and non-exhaustive embodiments of the present embodiments are described with reference to the following figures, wherein like reference numerals refer to like parts throughout the various views unless otherwise specified.
Hereinafter, example embodiments will be described in more detail with reference to the accompanying drawings, in which like reference numbers refer to like elements throughout. The present disclosure, however, may be embodied in various different forms, and should not be construed as being limited to only the illustrated embodiments herein. Rather, these embodiments are provided as examples so that this disclosure will be thorough and complete, and will fully convey the aspects and features of the present disclosure to those skilled in the art. Accordingly, processes, elements, and techniques that are not necessary to those having ordinary skill in the art for a complete understanding of the aspects and features of the present disclosure may not be described. Unless otherwise noted, like reference numerals denote like elements throughout the attached drawings and the written description, and thus, descriptions thereof may not be repeated. Further, in the drawings, the relative sizes of elements, layers, and regions may be exaggerated and/or simplified for clarity.
Pose estimation generally refers to a computer vision technique for estimating or predicting the location and orientation of objects. Some forms of pose estimation refer to detecting the physical pose of a human figure, such as the position and orientation of a person's head, arms, legs, and joints. Pose estimation may also refer more generally to the position and orientation of various animate or inanimate physical objects in a scene. For example, autonomously navigating robots may maintain information regarding the physical poses of objects around them in order to avoid collisions and to predict trajectories of other moving objects. As another example, in the case of robotics for use in manufacturing, object pose estimation may be used by robots to detect the position and orientation of physical manufacturing components, such that a robot arm can approach the component from the correct angle to obtain a proper grip on the part for assembly with other components of a manufactured product (e.g., gripping the head of a screw and threading the screw into a hole, whereas gripping a screw by the tip would make it difficult to insert into a hole).
Robot arms may be configured with different types of end effectors (also referred to as grippers) that may be used for different pick-and-place tasks. For soft robots, the end effectors may be flexible or adaptable to conform to the shape of an object to be picked, without active position control. Such compliance in grasping may be desirable to avoid shocks that could damage the target object to be picked, or push it out of the desired path. Soft robotic end effectors may include, for example, an array of pins, tubes, or suction cups. Soft robotic end effectors may also be formed of resilient materials such as rubber, polymers, and/or the like.
In one embodiment, a vision guided gripping system leverages information of various objects in a scene provided by a computer vision system, to adjust a soft robotic gripper to a shape that is predicted to provide an optimal grip of a target object. The soft robotic gripper may maintain the shape as the gripper approaches the target object, such as, for example, right up to the point of grasping and/or lifting the target object. Such pre-shaping of the robotic gripper may be desirable, for example, in a cluttered environment to avoid obstacles, and to focus the pick on the target object as opposed to other objects that may be blocking the target object.
In one embodiment, an optimal shape of the soft robotic gripper is based on visible grasp points on the target object. The optimal shape of the gripper may be re-adjusted any time prior to the gripping of the target object, based on changes to the scene as detected by the vision system, and/or based on progress along a motion path to the target object.
In one embodiment, the vision guided gripping system includes a vision system with one or more cameras 1a, 1b (collectively referenced as 1) configured to capture images of the scene. One or more of the cameras may be, for example, depth cameras (e.g. passive stereo cameras or active stereo cameras with structured light for computing depth from stereo, time-of-flight depth cameras, LIDAR, and the like). The one or more cameras may have the same or different imaging modalities to capture the images of the scene. Examples of imaging modalities include, without limitation, monochrome, color, infrared, near-infrared (NIR), ultraviolet, thermal, polarization, and combinations thereof. In one embodiment, the one or more cameras 1 include a polarization camera that uses a polarization imaging modality. In this regard, the polarization camera may be equipped with a polarizer or polarizing filter or polarization mask that is configured to enable the polarization camera to capture images of the scene with the polarizer set at various specified polarization angles (e.g., spaced apart at 45° rotations or at 60° rotations or at non-uniformly spaced polarization angles).
The vision guided gripping system may also include a robot arm 4 coupled to one or more end effectors/grippers 5. Although a robot arm 4 with a pin array end-effector 5 is used as an example, a person of skill in the art should recognize that the embodiments of the present disclosure extend to any automated apparatus configured to handle objects such as, for example, any type of robot or robotic manipulator, automated vehicles with lift capabilities, lift modules, gantries, and/or the like. Also, although exemplary embodiments are described in connection with bin-picking, a person of skill in the art should recognize that the present embodiments are not so limited, and may be used in a variety of applications.
In one embodiment, the one or more end effectors 5 are soft robotic end effectors formed of material and/or having structure that may be fully or partially molded into a desired shape. In this regard, the end effector 5 may have a base 5a and one or more grasp members 5b. The base 5a may include, for example, an actuation system for actively driving the grasp members 5b during pre-shaping, grasping, and the like. The one or more grasp members 5b may be, without limitation, an array/matrix of pins, tubes, suctions cups, and/or the like (collectively referred to as “pins”). In some embodiments, the one or more grasp members 5b may be made of silicone or other flexible material, and/or comprise underactuated joints as described in J. Shintake et al. “Soft Robotic Grippers,” Advanced Materials, Vol. 30, 1707035 (2018), the content of which is incorporated herein by reference.
In one embodiment, all or a portion of the end effector 5 is configured to deform passively when the end effector comes into contact with a target object, and conform, at least in part, to the shape of the surface that is touched. The end effector 5 may also be configured for active deformation in response to the pins being actively driven during a pre-shaping process, prior to making contact with the target object. In one embodiment, the end effector 5 is pre-shaped based on the 3D shape of a target object. The 3D shape may be determined based on the images of the scene captured by the one or more cameras 1. In one embodiment, one or more of the grasp members 5b are slid in and/or out of the base 5a to shape the end effector to a desired shape that is determined based on the 3D shape of the target object.
In one embodiment, one or more sensors are disposed in one or more locations of the robot arm 4 and/or end effector 5. The sensors may include, without limitation, Hall-effect sensors, encoders, torque sensors, tension sensors, and/or other sensors for estimating position and velocity of the robot arm 4 and end effector 5. The sensors may also include pressure sensors, resistive and conductive sensors, electromagnetic sensors, and/or other sensors for gathering, along with the one or more cameras 1, information about the objects 2 in the scene. For example, the sensors may provide tactile information in response to the end effector grasping a target object.
In one embodiment, the images captured by the one or more cameras 1 are supplied to a computing system 6 for executing the vision guided gripping by the end effector 5. The computing system 6 may include, without limitation, a vision module 7, shape prediction module 108, motion planning module 9, and control module 11. Although the various modules 7-11 are assumed to be separate functional units, a person of skill in the art will recognize that the functionality of the modules may be combined or integrated into a single module, or further subdivided into further sub-modules without departing from the spirit and scope of the inventive concept.
The vision module 7, shape prediction module 8, and/or motion planning module 9 may include one or more neural networks, such as, for example, one or more convolutional neural networks (CNN), recurrent neural networks (RNN), long short-term memory (LSTM) recurrent neural networks, gated recurrent units (GRUs) and/or the like. The neural network that is employed may include different number of layers and different number of nodes within each layer of the neural network. The one or more neural networks may be trained, among other things, to generate predictions on the shape of the end effector 5 for optimal grasping.
In one embodiment, the vision module 7 is configured to process images provided by the cameras 1 for obtaining information of the objects 2 in the scene. In this regard, the vision module 7 may be configured to perform object segmentation, surface normal calculation, depth estimation, pose estimation, and/or the like. The information obtained by the vision module 7 may include information such as the shape, surface normal, pose, texture, and/or keypoints of the objects 2 in the scene. In one embodiment, object segmentation entails generating a segmentation map where each pixel of the segmentation map is associated with one or more confidences that a pixel in an input image corresponds to various possible classes (or types) of objects. In one embodiment, pose estimation may be performed in six degrees of freedom as described below in the section entitled “POSE DETECTION AND MEASUREMENT.”
In one embodiment, the vision module 7 is configured to identify the 3D shape of one or more objects 2 in the scene, based on the information obtained for the objects. The 3D shape of a particular object may be computed based on the segmentation maps of multiple images of the scene from different view points captured by multiple cameras 1. The 3D shape may also be obtained by retrieving a precomputed 3D model (e.g. a CAD model) based on the segmentation map for the particular object, and aligning of the 3D model based on a calculated pose of the particular object.
In one embodiment, the shape prediction module 8 may be configured to predict a shape of the end effector 5 based on the 3D shape of the objects 2 in the scene, for pre-shaping the end effector 5 prior to attempt a pick of a target object. In this regard, one or more neural networks of the shape prediction module 8 may take as input information of the objects 2 in the scene provided by the vision module 7 (e.g. 3D geometry of the objects), along with optional other parameters such as, for example, grasp/suction scores based on material properties, object texture information, angle of attack, and/or motion paths. The output of the shape prediction module 8 may be, for example, one or more predicted shapes of the end effector 5, along with associated probability values indicative of a successful grasp.
In one embodiment, the predicted shape of the end effector is one that maximizes surface contact area of the target object while avoiding other objects. In this regard, the shape of the end effector 5 may mimic the shape of the visible/accessible areas of the target object for the portion of the end effector configured to make contact with the target object, and take a shape that avoids contact with non-target objects for the portion of the end effector that may otherwise make contact with the non-target objects.
In one embodiment, the shape of the end effector is one that achieves contact with a maximum number of visible grasp points of the target object (while again, avoiding contact with other objects). The grasp points may be, for example, points on the target object that are graspable by the end effector 5 to achieve a pick. An example grasp point for a screw may be an edge of the head of the screw. In some embodiments, the grasp points may be predefined for each type of 3D shape possible in the scene. In some embodiments, the grasp points may be identified via machine learning based on successes and failures of pick attempts. The grasp points may also differ depending on the type of end effector 5 that is being used to grasp the object (e.g., suction cups versus pliable silicone grasp members at the tips of the pins of a pin array).
In some embodiments, each grasp point may be associated with a grasp score indicative of a predicted success of a pick that uses the grasp point or collection of grasp points. In this regard, the shape of the end effector may be one that maximizes the grasp score.
Other factors may also be considered in predicting a shape of the end effector to achieve an optimal grasp. For example, texture and/or surface normal of the target object may be considered so that the shape of the end effector is one that maximizes contact of surface areas of the target object with certain textures, and/or applying force to the object along directions identified as the surface normals of the grasp points. For example, for end effectors consisting of an array of suction cups, the shape of the end effector may be one that maximizes contact with smooth areas of the target object and/or may approach the smooth areas along the direction of the surface normal (e.g., perpendicular to the surface). In one embodiment, a grasp score may be assigned to one or more portions of the object based on texture, surface normal computations, and the like.
The motion planning module 9 may be configured to generate a motion plan for moving the robot arm 4 to complete a given task. The task may be, for example a bin picking task where the robot arm 4 to picks up a target object from a source location, and places the target object at a destination location. In this regard, the motion plan may include commands to manipulate the end effector 5 to take a particular pose and/or angle of attack, and perform the pick-and-place task. Such commands may include, for example, turning, bending, grasping, lifting, placing, and/or the like.
In one embodiment, the motion planning module 9 executes a motion planning algorithm to generate the motion plan. In this regard, the motion planning algorithm may take as input, parameters and/or constraints associated with the given task, and output a corresponding motion plan (e.g. a list of motion commands) based on the input. The output motion plan may be one that is predicted to be optimal. One of existing motion planning algorithms may be employed for generating the optimal motion path, such as, for example, A*, D*, Rapidly-exploring Random Tree (RRT), Probabilistic Roadmap, or the like.
In one embodiment, one of the constraints input to the motion planning algorithm is the predicted shape of the end effector 5. In this regard, according to one embodiment, the shape prediction module 8 first predicts an optimal shape of the end effector 5, and the motion planning algorithm then optimizes the motion plan based on, among other constraints, the predicted shape. In some embodiments, a single algorithm may jointly solve for an optimal motion plan as well as for an optimal shape of the end effector 5.
In some cases, more paths may become available with the pre-shaping of the end effector 5 than without pre-shaping. For example, the pre-shaping of the end effector 5 may avoid certain obstacles in a given path, making that path available for consideration by the motion planning algorithm. In some embodiments, the pre-shaping of the end effector 5 may be based on the shape of an obstacle in a given path instead of the shape of the target object to be picked. Once the end effector 5 passes the obstacle, the end effector may then be re-shaped to the shape of the target object.
In some embodiments, if the pick is unsuccessful, the shape of the end effector and/or motion plan may be altered to attempt the pick again. Failures and successes of the picks may be used as feedback to train the shape prediction module 8 and/or motion planning module 9 accordingly.
In one embodiment, the control module 11 generates commands to one or more controllers of the robot arm 4 and/or end effector 5 according to the motion plan output by the motion planning module 9. The one or more controllers may include, without limitation, one or more actuation systems that control movement of the robot arm 4 and/or end effector 5. For example, the actuation system of the end effector 5 may include motors, pneumatic actuators, magnetic actuators, hydraulic actuators, and/or the like.
In one embodiment, the controllers may cause the end effector to move (or be adjusted) from a first state to a second state, based on the commands from the control module 11. The first state may be a resting equilibrium position where the controller generates zero force or zero torque. The second state may be a non-equilibrium state that deviates from the equilibrium position based on the predicted shape. In this regard, the actuation system of the end effector 5 may drive the one or more grasp members 5b to protrude and/or retract based on the predicted shape. In one embodiment, the end effector 5 may turn rigid to maintain the predicted shape.
In one embodiment, the controllers may cause the end effector to continue to maintain the rigid shape as the end effector 5 approaches a target object to be grasped. In this regard, the actuation system may exert force on the grasp members 5b to cause the grasp members 5b to maintain the predicted shape. The predicted shape may be maintained up until the point of contact of the end effector 5 with the target object. In some embodiments, all or a portion of the end effector 5 may revert back to the equilibrium state in response to making contact with the surface of the target object. For example, the portion of the end effector contacting the target object may regain flexibility, and passively adapt to the shape of the object from the neutral position in response to making the contact. Once the end effector has adapted to the shape of the target object, the end effector may be driven (e.g. by pushing, pressing, and/or pressurizing the grasp members 5b) to become rigid to securely grasp the object.
According to various embodiments of the present disclosure, the computing system is implemented using one or more electronic circuits configured to perform various operations as described in more detail below. Types of electronic circuits may include a central processing unit (CPU), a graphics processing unit (GPU), an artificial intelligence (AI) accelerator (e.g., a vector processor, which may include vector arithmetic logic units configured efficiently perform operations common to neural networks, such dot products and softmax), a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a digital signal processor (DSP), or the like. For example, in some circumstances, aspects of embodiments of the present disclosure are implemented in program instructions that are stored in a non-volatile computer readable memory where, when executed by the electronic circuit (e.g., a CPU, a GPU, an Al accelerator, or combinations thereof), perform the operations described herein for a vison guided gripper. The operations performed by the computing system 6 may be performed by a single electronic circuit (e.g., a single CPU, a single GPU, or the like) or may be allocated between multiple electronic circuits (e.g., multiple GPUs or a CPU in conjunction with a GPU). The multiple electronic circuits may be local to one another (e.g., located on a same die, located within a same package, or located within a same embedded device or computer system) and/or may be remote from one other (e.g., in communication over a network such as a local personal area network such as Bluetooth®, over a local area network such as a local wired and/or wireless network, and/or over wide area network such as the internet, such a case where some operations are performed locally and other operations are performed on a server hosted by a cloud computing service). One or more electronic circuits operating to implement the computing system 6 may be referred to herein as a computer or a computer system, which may include memory storing instructions that, when executed by the one or more electronic circuits, implement the systems and methods described herein.
The extracted derived feature maps 17 may be provided as input to the predictor 19 to compute the prediction output 21. In one embodiment, the predictor 19 is an image segmentation or instance segmentation system, and the prediction output 21 may be a segmentation map (e.g. an instance segmentation map). One class of approaches to performing instance segmentation on input images is to supply input images to a convolutional neural network (CNN) that is trained to compute instance segmentation maps from those input images. Examples of image segmentation CNNs include Mask R-CNN (He, Kaiming, et al. “Mask R-CNN.” Proceedings of the IEEE International Conference on Computer Vision. 2017.), AlexNet (see, e.g., Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. “ImageNet classification with deep convolutional neural networks.” Advances in neural information processing systems. 2012.), VGG (see, e.g., Simonyan, Karen, and Andrew Zisserman. “Very deep convolutional networks for large-scale image recognition.” arXiv preprint arXiv:1409.1556 (2014).), ResNet-101 (see, e.g., Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 770-778, 2016.), MobileNet (see, e.g., Howard, Andrew G., et al. “Mobilenets: Efficient convolutional neural networks for mobile vision applications.” arXiv preprint arXiv:1704.04861 (2017).), MobileNetV2 (see, e.g., Sandler, Mark, et al. “MobileNetV2: Inverted residuals and linear bottlenecks.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018.), and MobileNetV3 (see, e.g., Howard, Andrew, et al. “Searching for MobileNetV3.” Proceedings of the IEEE International Conference on Computer Vision. 2019.)
In some embodiments, the predictor 19 is a classification system, and the prediction output 21 includes a plurality of classes and corresponding confidences that the input images 13 depict an instance of each of the classes. In yet some embodiments, the predictor 19 is a classical computer vision prediction algorithm, and the prediction output includes detected features such as, for example, detected edges, keypoints, grasp points, basis coefficients, Haar wavelet coefficients, or other features of the objects in the image.
The end effector 5 may transition from the equilibrium state to a pre-shaping state in response to the shape prediction module 8 outputting a predicted shape. (
In one embodiment, the flexible grasp members 5b become rigid in response to the pre-shaping. The rigid pre-shaped shape may be maintained as the end effector 5 approaches the target object 2b. In one embodiment, at least a portion of the grasp members 5b′ become flexible again during a pick process in order for the grasp members 5b′ to passively mold to the surface of the target object to be picked. (
At block 55, the vision module 7 computes or retrieves the 3D shape of the objects in the scene based on the one or more segmentation maps. The 3D shapes may be overlaid in the scene according to a computed pose of the corresponding objects.
At block 57, the shape prediction module 8 predicts a shape of the end effector 5 based on one or more of the identified 3D shapes. The shape of the end effector 5 may be defined, for example, using a protrusion value for each of the grasp members 5b indicative of how far the grasp members 5b protrude out of the base 5a.
At block 59, the motion planning module 9 generates a motion plan for picking up a target object from the bin 3. The motion plan may be configured to optimize a path to be taken by the robot arm 4 based on, among other constrains, the obstacles in the scene, the predicted shape of the end effector 5, and/or the like.
At block 61, the control module 11 pre-shapes the end effector 5 based on the shape output by the shape prediction module 8. In one embodiment, the pre-shaping may be part of the motion plan output by the motion planning module 9. In this regard, the motion plan may call for the pre-shaping of the end effector 5 prior to approaching the target object.
At block 63, the robot arm 4 approaches the target object according to the motion plan. In this regard, the end effector 5 maintains the pre-shaped shape during the approach stage. The robot arm 4 attempts a pick of the target object when the robot arm 4 is at a pick location, according to the motion plan.
At block 65, a determination is made as to whether a pick was successful. If the answer in NO, a feedback is provided, at block 67, to the shape prediction module 8 and/or motion planning module 9 for indicating that the pick was unsuccessful, for further training of the shape prediction module 8 and/or motion planning module 9. In one embodiment, the process repeats to re-determine the shape of the end effector 5 and/or motion plan using updated images of the scene. In some embodiments, the end effector 5 may be controlled to clear obstructing objects near the target object prior to repeating the process.
Referring again to block 65, if a determination is made that the pick was successful, a feedback is provided, at block 69, to the shape prediction module 8 and/or motion planning module 9 for validating the predicted shapes and/or motion plan.
In one embodiment, a pick may be deemed to be successful in response to testing stability of the pick. In this regard, the robot arm 4 and/or end effector 5 may be configured to shake the grasped object and measure any displacement of the object in response to the shaking. One or more tactile sensors on the end effector 5 may be invoked to measure the displacement. In one embodiment, the pick is deemed to be successful in response to the displacement in the vertical axis being under a set threshold value.
In some embodiments, the shape of the end effector 5 may change as the robot arm 4 progresses along a motion path based on, for example, updated images of the scene provided by the one or more cameras 1. For example, the end effector 5 may be initially pre-shaped based on a shape of an obstacle in the motion path. The pre-shaping may allow the robot arm 4 to efficiently navigate around the obstacle. In this manner, the motion path with an obstacle that would otherwise not be available to be selected by the motion planning module 9, may be selected if deemed to be the most optimal.
In one embodiment, updated images of the scene after the robot arm 4 safely navigates around the obstacle may trigger a re-shaping of the end effector 5 to be pre-shaped based on the shape of another obstacle in the motion path, or the shape target object (and surrounding objects) to be picked. The shaping and re-shaping may continue until the robot arm 4 achieves a successful pick. The dynamic updating of the pre-shape form of the end effector 5 based on updated images of the scene and/or based on reaching of particular milestones (e.g. moving past obstacles), allows for a soft robotic gripper that is more versatile and better fit for use for bin picking tasks.
In some embodiments, the computing system 6 may be configured to select an end effector 5 from a plurality of available end effectors based on information of the objects in the scene computed by the vision module 7. A type of end effector 5 that is configured to provide an optimal grasp result may be selected. For example, an array of suction cups may be selected instead of an array of pins in response to the vision module 7 determining that the texture of the target object to be picked is smooth. A machine learning algorithm may be invoked to learn the most optimal end effector to be used for a given target object.
Pose Detection and Measurement
Pose estimation generally refers to a technique for estimating or predicting the location and orientation of objects. Some forms of pose estimation refer to detecting the physical pose of a human figure, such as the position and orientation of a person's head, arms, legs, and joints. Pose estimation may also refer more generally to the position and orientation of various animate or inanimate physical objects in a scene. For example, autonomously navigating robots may maintain information regarding the physical poses of objects around them (e.g., humans, vehicles, equipment, other robots, barriers, doors, and the like) in order to avoid collisions and to predict trajectories of other moving objects. As another example, in the case of robotics for use in manufacturing, pose estimation may be used to detect the position and orientation of components and workpieces such that a robotic arm can approach the components and workpieces from the correct angle to obtain a proper grip on the part for assembly with other components of a manufactured product (e.g., gripping the head of a screw and threading the screw into a hole, whereas gripping a screw by the tip would make it difficult to insert into a hole, or gripping a flexible printed circuit, flexible circuit, or flex circuit and attaching the ends of the connector to different components of the manufactured product, such as connecting a flexible printed circuit to two different rigid circuit boards) and orient and/or reorient components and workpieces for assembly.
Aspects of embodiments of the present disclosure relate to systems and methods for automated six degree of freedom (6-DoF) estimation of a wide variety of objects in a scene. The six degrees of freedom in three dimensional space include positional coordinates (e.g., x, y, and z translational coordinates in a three-dimensional global coordinate system) and orientation coordinates (e.g., 8, 4), and ip rotational coordinates in the three-dimensional global coordinate system).
Different pose estimation systems exhibit different levels of accuracy and precision in their measurements. The precision of such pose estimation systems may depend, for example, on signal-to-noise ratios, and the accuracy of the measurements may depend on parameters such as the resolution of the sensing devices. More concretely, in the case of an active scanning system such as lidar, the resolution of the sensing depends on the scanning rate of the active scanner as it sweeps over the surfaces of the objects in a scene, where there is a tradeoff between faster scans that produce lower resolution images or slower scans that produce higher resolution images. As another example, the resolution of a camera-based pose estimation system may be limited by the resolution of the image sensor in the camera (or cameras), the field of view of the lens over the scene, and the distance to the surfaces in the scene.
Small objects pose a particular challenge because the error margins of comparative pose estimation systems may be comparable in size to the dimensions of those small objects. For example, some comparative pose estimation systems have a pose estimation error of about 10 millimeters at a nominal working distance of 1000 meter. When objects are relatively large, such as about 100 mm in diameter, this error of 10 mm may be acceptable and within the tolerances for a robotic gripper to pick up the object. However, a 10 mm error is extremely high when the objects are relatively small, such as about 15 mm in diameter, and may cause the gripper to miss the object entirely or attempt to grasp a non-graspable portion of the object.
When estimating the pose of small objects using comparative pose estimation systems, one approach would be to place the camera as close as possible to the subject, as this would effectively increase the effective resolution of the images of the object. However, the operating environment may make it impractical or impossible to place the camera close enough to achieve the desired precision and accuracy. For example, the placement of the camera may be constrained (to be out of the way of moving machines), the camera might need to see a cluster or group of objects all at once (so it cannot be narrowly focused on one single object), or the location of the objects may be difficult to predict ahead of time (so the camera must be able to see all possible locations where the objects could be located). Thus, physically small objects also tend to be visually small in the camera's field of view. Increasing the resolution of the image capture process may increase accuracy, but have tradeoffs in the form of increasing a cycle time (e.g., a time between starting to image the scene containing objects and outputting a computed pose) due to increases in scanning time (e.g., for active scanning systems such as lidar), increases in processing time (e.g., data bandwidth and processing time for executing algorithms on high resolution images), and/or increases in hardware and energy costs (e.g., higher resolution image sensors, faster processors, additional processing cores, and the like).
For the sake of discussion, in the context of the typical resolutions of imaging systems (e.g., cameras) and a nominal working distance of about 1 meter, “small object” refers to any object which is no larger than about 30 mm in diameter, but embodiments are not limited thereto and are applicable in other situations where the objects appear visually small within the field of view of the sensing system (e.g., where the pixel resolution of the portion of the image depicting the object is relatively small) due to factors such as the relative size of objects, the working distance, and constraints of the imaging systems (e.g., image sensor resolution, field of view, scanning rates to achieve desired cycle times, and the like), which contribute to the pose estimation error of comparative pose estimation systems to be too large for particular applications, such as being insufficiently accurate to control a robot arm to perform a task of manipulating the small objects.
Some approaches to estimating the 6-DoF poses of objects involve aligning a given 3-D model of the object with the object as observed in the environment. This enables the robotic system to determine the pose of the physical object based on the virtual pose of the aligned 3-D model. In the most commonly used datasets for pose estimation (e.g., LineMOD as described in Hinterstoisser, Stefan, et al. “Model based training, detection and pose estimation of texture-less 3d objects in heavily cluttered scenes.” Asian conference on computer vision. Springer, Berlin, Heidelberg, 2012., YCB-Video as described in Xiang, Yu, et al. “PoseCNN: A convolutional neural network for 6d object pose estimation in cluttered scenes.” arXiv preprint arXiv:1711.00199 (2017).), all the objects in these datasets are at least 85 mm in diameter and most are within the 120 mm to 200 mm diameter range. Comparative methods for estimating the poses of objects in these datasets report over 95% accuracy (e.g., Bukschat, Yannick, and Marcus Vetter. “EfficientPose—An efficient, accurate and scalable end-to-end 6D multi object pose estimation approach.” arXiv preprint arXiv:2011.04307 (2020). and Zakharov, Sergey, Ivan Shugurov, and Slobodan Ilic. “DPOD: 6d pose object detector and refiner.” Proceedings of the IEEE/C VF International Conference on Computer Vision. 2019.) in detecting the poses of these objects, where a pose estimate is considered to be “correct” if it is within 10% of the object's diameter (e.g., within 8.5 mm to 20 mm, assuming an object diameter of 85 mm to 200 mm). However, errors in the range of 8.5 mm to 20 mm are far too large for the reliable picking up of small objects (e.g., with a diameter smaller than about 30 mm).
In addition to locating or estimating the poses of rigid objects, some aspects of embodiments of the present disclosure are applied to determining the configuration or deformed shape of deformable objects. Estimating the 6-DoF poses of deformable objects is useful in the field of robotics, such as in robotic systems that manipulate deformable objects. In particular, robotic systems may use the 6-DoF poses of objects in a scene to determine which of the objects are graspable. (An object may be considered to be graspable if it is not blocked by other objects and having mechanically stable surfaces that can be grasped by the end effector of a robotic arm without damaging the object). The robotic system may then grasp a detected graspable object and manipulate that object in some way (e.g., attach a flexible component to an object of manufacture, pick a deformable item and pack the deformable item into a box for shipping, or maintain control of a deformable object during transport). Robotic systems may also be commonly applied to bin packing or placing deformable items into a bin (such as a rigid box for shipping). Examples of such deformable objects include food packaging (bags of chips, candy, etc.), mechanical springs, folded clothing, and the like.
Some approaches to estimating the 6-DoF poses of objects involve aligning a given 3-D model of the object with the object as observed in the environment. This enables the robotic system to determine the pose of the physical object based on the virtual pose of the aligned 3-D model. However, in the case of deformable objects, these existing 3-D models may not be representative of the actual 3-D configurations of the objects in the real world. For example, a 3-D model of a rope may depict the rope in a bundled state, but the actual rope may be folded or twisted, such that the 3-D model of the rope is not representative of the physical rope that is presented to the robotic system. Likewise, a 3-D model of a flex circuit may depict the flex circuit in a flat or substantially planar shape, whereas the flex circuit that is present in the environment may be curved or bent at various portions, due to interactions with external forces such as gravity and other objects in contact with the flex circuit. The process of grasping the object may deform the object from its configuration prior to grasping, and the configuration of the object may further change in the course of manipulating the object (e.g., through interaction with gravity and other forces in the environment).
Some aspects of embodiments of the present disclosure relate to detecting the poses of deformable objects having three-dimensional shapes that can vary continuously through a range of possible configurations. The term “configuration” may be used herein to refer to a physical arrangement of different parts of an object with respect to an object coordinate system (as opposed to a world or global coordinate system). For example, a rigid object may be considered to have a single “configuration,” as the term is used herein, even though its pose within its external environment can be varied (e.g., the rigid object can be rotated and positioned with six degrees of freedom in the external environment). On the other hand, a hinge may have an infinite number of possible configurations because the angle between the components on the opposite sides of the hinge may vary continuously between the extremes of the range of motion. Likewise, a rope may have an infinite number of configurations because every point along the length of the rope may be bent and/or twisted as constrained by the flexibility or pliability of the rope. The configuration of an object may alternatively be referred to herein as a “physical configuration” and/or an “object configuration.”
As such, aspects of embodiments of the present disclosure relate to systems and methods for increasing the accuracy of the detection of locations of objects, such as increasing the accuracy of estimated poses of objects and estimating the deformed shape or configuration of deformable objects. In particular, aspects of embodiments of the present disclosure enable the accurate location (e.g., pose estimation) of small objects in a scene, such as circumstances where constraints including image resolution, image capture speed, field of view of the imaging, and cycle time cause portions of the captured images corresponding to individual objects to be visually small (e.g., low resolution). In addition, in some embodiments, systems and methods described herein are integrated as components of a processing pipeline that may be trained, in an end-to-end fashion, to control robotic systems into interact with objects in the environment, without explicitly calculating a location of the object (e.g., a 6-DoF pose of the object) within the environment.
In the case of estimating or predicting a 6-DoF pose of an object, the six degrees of freedom in three dimensional space include positional coordinates (e.g., x, y, and z translational coordinates in a three-dimensional global coordinate system) and orientation coordinates (e.g., 8, 4), and ip rotational coordinates in the three-dimensional coordinate system). A pose estimation system according to embodiments of the present disclosure, may combine the six-dimensional pose of an object within the scene with a 3-D model of the object (e.g., a 3-D mesh model of the object such as a computer aided design or CAD model, where the mesh may include a collection of vertices and edges connecting the vertices, each of the vertices having three-dimensional coordinates (e.g., x, y, z coordinates), and where the three-dimensional coordinates may be represented in an object coordinate system relative to the object itself or a global coordinate system relative to some external environment). In the case of deformable objects, some aspects of embodiments of the present disclosure relate to identifying and/or generating a 3-D model of the object that corresponds to the configuration of the object (e.g., the relative three dimensional positions of the vertices of the 3-D model of object, thereby defining the observed deformed shape or configuration of the deformable object).
While embodiments of the present disclosure are particularly suited to improving the detection and location (e.g., pose estimation) of small objects, applications of embodiments are not limited thereto and the systems and methods described herein may also be applied to locating and/or estimating the poses of larger objects. Furthermore, the systems and methods described herein may be applied to estimating the physical configurations of deformable objects.
Some aspects of embodiments of the present disclosure relate to computing dense correspondences as part of a processing pipeline for estimating the locations (e.g., poses) of objects depicted in scenes. However, embodiments of the present disclosure are not limited thereto.
Generally, optical flow relates to the distribution of apparent velocities of movement of brightness patterns in an image (see, e.g., Horn, Berthold KP, and Brian G. Schunck. “Determining optical flow.” Artificial intelligence 17.1-3 (1981): 185-203.). One common use of optical flow relates to detecting the movement of objects between successive image frames of a video, such as detecting the motion of a soccer ball based on the change of position of the brightness patterns associated with the ball (e.g., black and white patches) from one frame to the next. An optical flow map may represent the velocities of each pixel value in a first image frame to a corresponding pixel in the second image frame. For example, the brightness at a point (x,y) in the first image at time t may be denoted as E(x,y,t), and this pixel may move by some distance (Δx, Δy) from time t associated with the first image frame to time t+Δt associated with the second frame. Accordingly, the optical flow map may include a velocity (u, v) for each point (x, y) in the first image frame, where u=dx/dt and v=dy/dt. One aspect of algorithms for computing optical flow fields relates to determining correct correspondences between pairs of pixels in the two images. For example, for any given point (x,y) in the first image, there may be many pixels in the second image having the same brightness, and therefore an optical flow algorithm will need to determine which pixel in the second image corresponds to the point (x,y) of the first image, even if the corresponding point in the second image has a different brightness or appearance due to changes in lighting, noise, or the like.
Aspects of embodiments of the present disclosure relate to the use of optical flow for computing dense correspondences in the context of refining an estimated pose of an object. For example, a pose estimation system may capture an image of a scene and compute an initial estimated pose of a known type of object depicted in the image. A 3-D model (or computer aided design or CAD model) of the object is then rotated and transformed based on the initial estimated pose, and a 2-D view of the 3-D model can then be rendered from the perspective of a virtual camera, where the virtual camera has the same position as the real camera with respect to the object. If the estimated pose of the object is the same as the actual pose of the object in the scene, then the image of the object and the rendering of the 3-D model should appear the same. However, rotational and translational errors in the initial pose estimate can result in a mismatch between the estimated position and the real position of the object. Supplying the rendered image of the 3-D model and the captured actual image of the object to a dense correspondence algorithm (such as an optical flow algorithm) computes a dense correspondence map (such as an optical flow map) that maps between pixels of the rendered image and the captured or observed image of the actual object. The rendered image and the captured or observed image may include any of color (e.g., RGB) images, monochrome images, surface normals maps, polarization feature maps (e.g., angle of linear polarization and/or degree of linear polarization), and combinations thereof, and the rendered image and the observed image may be different types of images or the same type of image.
The computed optical flow map represents a dense correspondence map, as optical flow correspondences are computed for every visible pixel of the object (e.g., every visible pixel of the object in the first image is mapped to a corresponding pixel in the second image). However, alternative techniques may be used to compute these dense correspondence maps. In various embodiments, this dense correspondence map is then used to refine the estimated pose of the object to align the estimated pose with the actual pose of the object, as described in more detail below, using techniques such as Perspective-n-Point (PnP) algorithms taking a classical computer vision approach (e.g., computing a pose based on the inputs without using a learned model). Generally, a classical PnP algorithm relies on matching n points between the 3-D model and the image of the object, where the use of larger numbers of points improves the accuracy and confidence of the computed pose. However, comparative techniques for identifying features in the 3-D model and the image of the object result in relatively sparse feature maps, such that n is small. Aspects of embodiments of the present disclosure overcome this deficiency by generating dense correspondence maps (e.g., through optical flow, disparity maps, or other techniques), thereby increasing the number of points that are matched between the image of the object and the 3-D model and, in some embodiments, enabling detection of the deformation or configuration of the shape of the object.
In some embodiments, one or more support cameras 30 are arranged at different poses around the scene containing the arrangement 20 of objects 22. Accordingly, each of the support cameras 30, e.g., first support camera 30a, second support camera 30b, and third support camera 30c, captures a different view of the objects 22 from a different view point (e.g., a first viewpoint, a second viewpoint, and a third viewpoint, respectively). While
A pose estimator 100 according to various embodiments of the present disclosure is configured to compute or estimate poses of the objects 22 based on information captured by the main camera 10 and the support cameras 30. According to various embodiments of the present disclosure, the pose estimator 100 is implemented using one or more processing circuits or electronic circuits configured to perform various operations as described in more detail below. Types of electronic circuits may include a central processing unit (CPU), a graphics processing unit (GPU), an artificial intelligence (AI) accelerator (e.g., a vector processor, which may include vector arithmetic logic units configured efficiently perform operations common to neural networks, such dot products and softmax), a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a digital signal processor (DSP), or the like. For example, in some circumstances, aspects of embodiments of the present disclosure are implemented in program instructions that are stored in a non-volatile computer readable memory where, when executed by the electronic circuit (e.g., a CPU, a GPU, an Al accelerator, or combinations thereof), perform the operations described herein to compute a processing output, such as an instance segmentation map or 6-DoF poses, from input polarization raw frames 18 (the underlying images captured by polarization cameras or cameras with polarization filters in their optical paths). The operations performed by the pose estimator 100 may be performed by a single electronic circuit (e.g., a single CPU, a single GPU, or the like) or may be allocated between multiple electronic circuits (e.g., multiple GPUs or a CPU in conjunction with a GPU). The multiple electronic circuits may be local to one another (e.g., located on a same die, located within a same package, or located within a same embedded device or computer system) and/or may be remote from one other (e.g., in communication over a network such as a local personal area network such as Bluetooth®, over a local area network such as a local wired and/or wireless network, and/or over wide area network such as the internet, such a case where some operations are performed locally and other operations are performed on a server hosted by a cloud computing service). One or more electronic circuits operating to implement the pose estimator 100 may be referred to herein as a computer or a computer system, which may include memory storing instructions that, when executed by the one or more electronic circuits, implement the systems and methods described herein.
In more detail, the main camera 10 and the support cameras 30 are configured to estimate the poses of objects 22 detected within their fields of view 12 (while
In particular, a “pose” refers to the position and orientation of an object with respect to a reference coordinate system. For example, a reference coordinate system may be defined with the main camera 10 at the origin, where the direction along the optical axis of the main camera 10 (e.g., a direction through the center of its field of view 12) is defined as the z-axis of the coordinate system, and the x and y axes are defined to be perpendicular to one another and perpendicular to the z-axis. (Embodiments of the present disclosure are not limited to this particular coordinate system, and a person having ordinary skill in the art would understand that poses can be mathematically transformed to equivalent representations in different coordinate systems.)
Each object 22 may also be associated with a corresponding coordinate system of its own, which is defined with respect to its particular shape. For example, a rectangular prism with sides of different lengths may have a canonical coordinate system defined where the x-axis is parallel to its shortest direction, z-axis is parallel to its longest direction, the y-axis is orthogonal to the x-axis and z-axis, and the origin is located at the centroid of the object 22.
Generally, in a three-dimensional coordinate system, objects 22 have six degrees of freedom—rotation around three axes (e.g., rotation around x-, y-, and z-axes) and translation along the three axes (e.g., translation along x-, y-, and z-axes). For the sake of clarity, symmetries of the objects 22 will not be discussed in detail herein, but may be addressed, for example, by identifying multiple possible poses with respect to different symmetries (e.g., in the case of selecting the positive versus negative directions of the z-axis of a right rectangular prism), or by ignoring some rotational components of the pose (e.g., a right cylinder is rotationally symmetric around its axis).
In some embodiments, it is assumed that a three-dimensional (3-D) model or computer aided design (CAD) model representing a canonical or ideal version of each type of object 22 in the arrangement of objects 20 is available. For example, in some embodiments of the present disclosure, the objects 22 are individual instances of manufactured components that have a substantially uniform appearance from one component to the next. Examples of such manufactured components include screws, bolts, nuts, connectors, and springs, as well as specialty parts such electronic circuit components (e.g., packaged integrated circuits, light emitting diodes, switches, resistors, and the like), laboratory supplies (e.g. test tubes, PCR tubes, bottles, caps, lids, pipette tips, sample plates, and the like), and manufactured parts (e.g., handles, switch caps, light bulbs, and the like). Accordingly, in these circumstances, a CAD model defining the ideal or canonical shape of any particular object 22 in the arrangement 20 may be used to define a coordinate system for the object (e.g., the coordinate system used in the representation of the CAD model).
Based on a reference coordinate system (or camera space, e.g., defined with respect to the pose estimation system) and an object coordinate system (or object space, e.g., defined with respect to one of the objects), the pose of the object may be considered to be a rigid transform (rotation and translation) from object space to camera space. The pose of object 1 in camera space 1 may be denoted as Pc
where the rotation submatrix R:
represents rotations along the three axes from object space to camera space, and the translation submatrix T:
represents translations along the three axes from object space to camera space.
If two objects—Object A and Object B—are in the same camera C coordinate frame, then the notation PCA is used to indicate the pose of Object A with respect to camera C and PCB is used to indicate the pose of Object B with respect to camera C. For the sake of convenience, it is assumed herein that the poses of objects are represented based on the reference coordinate system, so the poses of objects A and B with respect to camera space C may be denoted PA and PB, respectively.
If Object A and Object B are actually the same object, but performed during different pose estimation measurements, and a residual pose Perr or PAB (PAB=Perr) is used to indicate a transform from pose PA to pose PB, then the following relationship should hold:
PAPerr=PB (1)
and therefore
Perr=PA−1PB (2)
Ideally, assuming the object has not moved (e.g., translated or rotated) with respect to the main camera 10 between the measurements of pose estimates PA and PB, then PA and PB should both be the same, and Perr should be the identity matrix (e.g., indicating no error between the poses):
In a similar manner, the pose of a particular object can be computed with respect to views from two different cameras. For example, images of Object A captured by a main camera C can be used to compute the pose PCA of Object A with respect to main camera C. Likewise, images of Object A captured by a first support camera S1 can be used to compute the pose PS
Ideally, assuming that the known relative poses of main camera C and support camera S1 are accurate and the poses calculated based on the data captured by the two cameras is accurate, then PCA and PS
Differences Perr between the actual measured value as computed based on the estimates computed by the pose estimator 100 and the identity matrix may be considered to be errors:
Rerr=∥R(Perr)∥ (3)
Terr=∥T(Perr)∥ (4)
where Rerr is the rotation error and Terr is the translation error. The function R( ) converts Perr into an axis-angle where the magnitude is the rotation difference, and the function T( ) extracts the translation component of the pose matrix.
The axis-angle representation from rotation matrix R is given by:
where Tr( )denotes the matrix trace (the sum of the diagonal elements of the matrix), and θ represents the angle of rotation.
Some aspects of embodiments of the present disclosure relate to computing a high accuracy pose estimate of objects 22 in a scene based on a joint estimate of the poses the objects across the main camera 10 and the support cameras 30, as described in more detail below.
Some aspects of embodiments of the present disclosure also relate to providing information to assist in the control of a robotic arm 24 having an end effector 26 that may be used to grasp and manipulate objects 22. The robotic arm 24, including its end effector 26, may be controlled by a robotic arm controller 28, which, in some embodiments, receives the six-degree-of-freedom poses computed by the pose estimator 100, which may include 3-D models representing various objects 22 in the scene 1, where the 3-D models have configurations that estimate or approximate the configurations of their corresponding real-world objects, noting, for example, that the configuration of portions of the objects 22 that are occluded or otherwise not visible in the fields of view 12 of the main camera 10 and support cameras 30 may be difficult or impossible to estimate with high accuracy.
While the sensor system is generally referred to herein as a pose estimator 100, embodiments of the present disclosure are not limited to computing poses (e.g., 6-DoF poses) of objects in a scene and may, instead of or in addition to computing 6-DoF poses, the sensor system, including one or more cameras (e.g., main camera and/or support cameras) and processing circuits may implement generalized vision systems that provide information to controller systems.
For example, a processing pipeline may include receiving images captured by sensor devices (e.g., master cameras 10 and support cameras 30) and outputting control commands for controlling a robot arm, where the processing pipeline is trained, in an end-to-end manner, based on training data that includes sensor data as input and commands for controlling the robot arm (e.g., a destination pose for the end effector 26 of the robotic arm 24) as the labels for the input training data.
Sensing Hardware
In the embodiment shown in
The main camera 10 and the support cameras 30 may use the same imaging modalities or different imaging modalities. Examples of imaging modalities include monochrome, color, infrared, ultraviolet, thermal, polarization, and combinations thereof.
The interaction between light and transparent objects is rich and complex, but the material of an object determines its transparency under visible light. For many transparent household objects, the majority of visible light passes straight through and a small portion (˜4% to ˜8%, depending on the refractive index) is reflected. This is because light in the visible portion of the spectrum has insufficient energy to excite atoms in the transparent object. As a result, the texture (e.g., appearance) of objects behind the transparent object (or visible through the transparent object) dominate the appearance of the transparent object. For example, when looking at a transparent glass cup or tumbler on a table, the appearance of the objects on the other side of the tumbler (e.g., the surface of the table) generally dominate what is seen through the cup. This property leads to some difficulties when attempting to detect surface characteristics of transparent objects such as glass windows and glossy, transparent layers of paint, based on intensity images alone:
Similarly, a light ray hitting the surface of an object may interact with the shape of the surface in various ways. For example, a surface with a glossy paint may behave substantially similarly to a transparent object in front of an opaque object as shown in
A light ray 43 hitting the image sensor 14 of a polarization camera 10 has three measurable components: the intensity of light (intensity image/I), the percentage or proportion of light that is linearly polarized (degree of linear polarization/DOLP/ρ), and the direction of that linear polarization (angle of linear polarization/AOLP/ϕ). These properties encode information about the surface curvature and material of the object being imaged, which can be used by the pose estimator 100 to detect transparent objects, as described in more detail below. In some embodiments, by using one or more polarization cameras, the pose estimator 100 can detect other optically challenging objects based on similar polarization properties of light passing through translucent objects and/or light interacting with multipath inducing objects or by non-reflective objects (e.g., matte black objects).
In more detail, the polarization camera 10 may further includes a polarizer or polarizing filter or polarization mask 16 placed in the optical path between the scene 1000 and the image sensor 14. According to various embodiments of the present disclosure, the polarizer or polarization mask 16 is configured to enable the polarization camera 10 to capture images of the scene 1000 with the polarizer set at various specified angles (e.g., at 45° rotations or at 60° rotations or at non-uniformly spaced rotations).
As one example,
While the above description relates to some possible implementations of a polarization camera using a polarization mosaic, embodiments of the present disclosure are not limited thereto and encompass other types of polarization cameras that are capable of capturing images at multiple different polarizations. For example, the polarization mask 16 may have fewer than four polarizations or more than four different polarizations, or may have polarizations at different angles than those stated above (e.g., at angles of polarization of: 0°, 60°, and 120° or at angles of polarization of 0°, 30°, 60°, 90°, 120°, and 150°). As another example, the polarization mask 16 may be implemented using an electronically controlled polarization mask, such as an electro-optic modulator (e.g., may include a liquid crystal layer), where the polarization angles of the individual pixels of the mask may be independently controlled, such that different portions of the image sensor 14 receive light having different polarizations. As another example, the electro-optic modulator may be configured to transmit light of different linear polarizations when capturing different frames, e.g., so that the camera captures images with the entirety of the polarization mask set to, sequentially, to different linear polarizer angles (e.g., sequentially set to: 0 degrees; 45 degrees; 90 degrees; or 135 degrees). As another example, the polarization mask 16 may include a polarizing filter that rotates mechanically, such that different polarization raw frames are captured by the polarization camera 10 with the polarizing filter mechanically rotated with respect to the lens 18 to transmit light at different angles of polarization to image sensor 14. Furthermore, while the above examples relate to the use of a linear polarizing filter, embodiments of the present disclosure are not limited thereto and also include the use of polarization cameras that include circular polarizing filters (e.g., linear polarizing filters with a quarter wave plate). Accordingly, in various embodiments of the present disclosure, a polarization camera uses a polarizing filter to capture multiple polarization raw frames at different polarizations of light, such as different linear polarization angles and different circular polarizations (e.g., handedness).
As a result, the polarization camera 10 captures multiple input images (or polarization raw frames) of the scene including the surfaces of the objects 22. In some embodiments, each of the polarization raw frames corresponds to an image taken behind a polarization filter or polarizer at a different angle of polarization ϕpol (e.g., 0 degrees, 45 degrees, 90 degrees, or 135 degrees). Each of the polarization raw frames is captured from substantially the same pose with respect to the scene 1000 (e.g., the images captured with the polarization filter at 0 degrees, 45 degrees, 90 degrees, or 135 degrees are all captured by a same polarization camera 10 located at a same location and orientation), as opposed to capturing the polarization raw frames from disparate locations and orientations with respect to the scene. The polarization camera 10 may be configured to detect light in a variety of different portions of the electromagnetic spectrum, such as the human-visible portion of the electromagnetic spectrum, red, green, and blue portions of the human-visible spectrum, as well as invisible portions of the electromagnetic spectrum such as infrared and ultraviolet.
In some embodiments, a demosaicing process is used to compute separate red, green, and blue channels from the raw data. In some embodiments of the present disclosure, each polarization camera may be used without a color filter or with filters used to transmit or selectively transmit various other portions of the electromagnetic spectrum, such as infrared light.
As noted above, embodiments of the present disclosure relate to multi-modal and/or multi-spectral camera arrays. Accordingly, in various embodiments of the present disclosure, the cameras within a particular camera array include cameras configured to perform imaging in a plurality of different modalities and/or to capture information in a plurality of different spectra.
As one example, in some embodiments, the first camera 10A′ is a visible light camera that is configured to capture color images in a visible portion of the electromagnetic spectrum, such as by including a Bayer color filter 16A′ (and, in some cases, a filter to block infrared light), and the second camera 10B′, third camera 10C′, and fourth camera 10D′ are polarization cameras having different polarization filters, such filters having linear polarization angles of 0°, 60°, and 120°, respectively. The polarizing filters in the optical paths of each of the cameras in the array cause differently polarized light to reach the image sensors of the cameras. The individual polarization cameras in the camera array have optical axes that are substantially perpendicular to one another, are placed adjacent to one another, and have substantially the same field of view, such that the cameras in the camera array capture substantially the same view of a scene as the visible light camera 10A′, but with different polarizations. While the embodiment shown in
As another example, one or more of the cameras in the camera array 10′ may operate in other imaging modalities and/or other imaging spectra, such as polarization, near infrared, far infrared, shortwave infrared (SWIR), longwave infrared (LWIR) or thermal, ultraviolet, and the like, by including appropriate filters 16 (e.g., filters that pass light having particular polarizations, near-infrared light, SWIR light, LWIR light, ultraviolet light, and the like) and/or image sensors 14 (e.g., image sensors optimized for particular wavelengths of electromagnetic radiation) for the particular modality and/or portion of the electromagnetic spectrum.
For example, in the embodiment of the camera array 10′ shown in
In some embodiments, the various individual cameras of the camera array are registered with one another by determining their relative poses (or relative positions and orientations) by capturing multiple images of a calibration target, such as a checkerboard pattern, an ArUco target (see, e.g., Garrido-Jurado, Sergio, et al. “Automatic generation and detection of highly reliable fiducial markers under occlusion.” Pattern Recognition 47.6 (2014): 390-402.) or a ChArUco target (see, e.g., An, Gwon Hwan, et al. “Charuco board-based omnidirectional camera calibration method.” Electronics 7.12 (2018): 421.). In particular, the process of calibrating the targets may include computing intrinsic matrices characterizing the internal parameters of each camera (e.g., matrices characterizing the focal length, image sensor format, and principal point of the camera) and extrinsic matrices characterizing the pose of each camera with respect to world coordinates (e.g., matrices for performing transformations between camera coordinate space and world or scene coordinate space). Different cameras within a camera array may have image sensors with different sensor formats (e.g., aspect ratios) and/or different resolutions without limitation, and the computed intrinsic and extrinsic parameters of the individual cameras enable the pose estimator 100 to map different portions of the different images to a same coordinate space (where possible, such as where the fields of view overlap).
In stereo camera array systems according to some embodiments, the camera arrays are spaced apart from one another such that parallax shifts between the viewpoints corresponding to the camera arrays are detectable for objects in the designed operating distance of the camera system. This enables the distances to various surfaces in a scene (the “depth”) to be detected in accordance with a disparity measure or a magnitude of a parallax shift (e.g., larger parallax shifts in the locations of corresponding portions of the images indicate that those corresponding portions are on surfaces that are closer to the camera system and smaller parallax shifts indicate that the corresponding portions are on surfaces that are farther away from the camera system). These techniques for computing depth based on parallax shifts are sometimes referred to as Depth from Stereo
Accordingly,
While some embodiments are described above wherein each array includes cameras of different types in a same arrangement, embodiments of the present disclosure are not limited thereto. For example, in some embodiments, the arrangements of cameras within a camera array are mirrored along an axis perpendicular to the baseline 10-B. For example, cameras 10A′ and 10F′ may be of a same first type, cameras 10B′ and 10E′ may be of a same second type, cameras 10C′ and 10H′ may be of a same third type, and cameras 10D′ and 10G′ may be of a same fourth type.
In a manner similar to that described for calibrating or registering cameras within a camera array, the various polarization camera arrays of a stereo camera array system may also be registered with one another by capturing multiple images of calibration targets and computing intrinsic and extrinsic parameters for the various camera arrays. The camera arrays of a stereo camera array system 10 may be rigidly attached to a common rigid support structure 10-S in order to keep their relative poses substantially fixed (e.g., to reduce the need for recalibration to recompute their extrinsic parameters). The baseline 10-B between camera arrays is configurable in the sense that the distance between the camera arrays may be tailored based on a desired or expected operating distance to objects in a scene—when the operating distance is large, the baseline 10-B or spacing between the camera arrays may be longer, whereas the baseline 10-B or spacing between the camera arrays may be shorter (thereby allowing a more compact stereo camera array system) when the operating distance is smaller.
As noted above with respect to
Measuring intensity I, DOLP ρ, and AOLP ϕ at each pixel requires 3 or more polarization raw frames of a scene taken behind polarizing filters (or polarizers) at different angles, ϕpol (e.g., because there are three unknown values to be determined: intensity I, DOLP ρ, and AOLP ϕ. For example, a polarization camera such as those described above with respect to
The relationship between Iϕ
Iϕ
Accordingly, with four different polarization raw frames Iϕ
Shape from Polarization (SfP) theory (see, e.g., Gary A Atkinson and Edwin R Hancock. Recovery of surface orientation from diffuse polarization. IEEE transactions on image processing, 15(6):1653-1664, 2006.) states that the relationship between the refractive index (n), azimuth angle (θa) and zenith angle (θz) of the surface normal of an object and the ϕ and ρ components of the light ray coming from that object follow the following characteristics when diffuse reflection is dominant:
and when the specular reflection is dominant:
Note that in both cases ρ increases exponentially as θz increases and if the refractive index is the same, specular reflection is much more polarized than diffuse reflection.
Accordingly, some aspects of embodiments of the present disclosure relate to applying SfP theory to detect or measure the gradients of surfaces (e.g., the orientation of surfaces or their surface normals or directions perpendicular to the surfaces) based on the raw polarization frames of the objects, as captured by the polarization cameras among the main camera 10 and the support cameras 30. Computing these gradients produces a gradient map (or slope map or surface normals map) identifying the slope of the surface depicted at each pixel in the gradient map. These gradient maps can then be used when estimating the pose of the object by aligning a pre-existing 3-D model (e.g., CAD model) of the object with the measured surface normals (gradients or slopes) of the object in based on the slopes of the surfaces of the 3-D model, as described in more detail below.
Estimating Six-Degree-of-Freedom Poses of Objects in a Scene
Estimating the six-degree-of-freedom (6-DoF) poses of objects in a scene is a useful task in various applications such as robotics, where understanding the three-dimensional (3-D) shapes and locations of objects in a scene provides more information to a robot controller regarding an environment, thereby improving situational awareness and enabling the robot controller to interact appropriately with the environment, in accordance the particular tasks assigned to the robot. As noted above, autonomously navigating robots or vehicles may maintain information about the poses of objects in a scene in order to assist with navigation around those objects in order to predict trajectories and to avoid collisions with those objects. As another example, in the case of manufacturing, pose estimation may be used by robotic systems to manipulate the workpieces and place and/or attach components to those workpieces.
Some aspects of systems and methods for estimating the six-degree-of-freedom poses of objects are described in International Patent Application No. PCT/US21/15926, titled “SYSTEMS AND METHODS FOR POSE DETECTION AND MEASUREMENT,” filed in the United States Patent and Trademark Office on Jan. 29, 2021, the entire disclosure of which is incorporated by reference herein. Generally, the approach described in the above-referenced international patent application relates to computing a 6-DoF pose of an object in a scene by determining a class or type of the object (e.g., a known or expected object) and aligning a corresponding 3-D model of the object (e.g., a canonical or ideal version of the object based on known design specifications of the object and/or based on the combination of a collection of samples of the object) with the various views of the object, as captured from different viewpoints around the object.
In operation 310, the pose estimator 100 controls one or more cameras, such as the master camera 10 and the support cameras 30, to capture one or more images of the scene, which may be from multiple viewpoints in the case of multiple cameras. In embodiments using multiple cameras, the cameras are configured to capture images concurrently or substantially simultaneously. Each camera is arranged at a different pose with respect to the scene 1, such that each camera captures scene from its corresponding different viewpoint. Accordingly, the collection of images captured by multiple cameras represent a collection of multi-viewpoint images of the scene 1. (In some embodiments, the images are captured from multiple viewpoints using one or more cameras, such as by moving the one or more cameras between different viewpoints while keeping the scene fixed, and/or rigidly transforming the scene between captures by the one or more cameras.) The one or more images of the scene may be referred to herein as being “consistent” in that they are all pictures of the same consistent scene but providing different views of the scene from different viewpoints and/or different imaging modalities. This consistency between the images of the scene may be achieved by capturing all of the images substantially simultaneously or concurrently or by requiring that none of the objects of interest in the scene that are depicted in the image have moved (e.g., translated or rotated) between in the time between the capture of different images of the scene.
In some circumstances, one or more of the “cameras” are multi-modal cameras that capture multiple images from the same viewpoint, but having in different modalities, such as different portions of the electromagnetic spectrum (e.g., red, green and blue portions of the visible light spectrum, near infrared light, far infrared light, ultraviolet light, etc.), different optical filters (e.g., linear polarization filters at different angles and/or circular polarization filters), and combinations thereof. Accordingly, a collection of multi-viewpoint images of a scene does not require that all images be captured from different viewpoints, but only that there are at least two images captured from different viewpoints. Such a collection of multi-viewpoint images therefore may include at least some images that are captured from the same viewpoint.
In the case of a sensing system using multi-viewpoint images or images of a scene from more than one viewpoint, in operation 330, the pose estimator 100 computes object-level correspondences on the multi-viewpoint images of the scene. More specifically, instances of one or more types of objects are identified in the multi-viewpoint images of the scene, and corresponding instances of objects are identified between the multi-viewpoint images. For example, a scene 1000 may include two cubes and three spheres, and various of the multi-viewpoint images may depict some or all of these five objects. A process of instance segmentation identifies the pixels in each of the images that depict the five objects, in addition to labeling them separately based on the type or class of object (e.g., a classification as a “sphere” or a “cube”) as well as instance labels (e.g., assigning a unique label to each of the objects, such as numerical labels “1,” “2,” “3,” “4,” and “5”). Computing object-level correspondences between the multi-viewpoint images further relates to computing consistent labels between the different viewpoints (for example, such that the same cube is labeled “1” from each of the viewpoint). Accordingly, the pose estimator 100 generates collections of crops or patches of the multi-viewpoint images of the scene, where each collection of patches depicts the same instance from different viewpoints (cropped to the region containing the object and, in some cases, a small neighborhood or margin around the object).
In the case of a single image depicting a scene from a single viewpoint, in operation 330, the pose estimator 100 may merely compute a segmentation map, which similarly enables the generation of a crop or patch for each object instance detected in the image.
Systems and methods for computing object-level correspondences are described in International Patent Application No. PCT/US21/15926, titled “SYSTEMS AND METHODS FOR POSE DETECTION AND MEASUREMENT,” filed in the United States Patent and Trademark Office on Jan. 29, 2021, which, as noted above, is incorporated by reference herein in its entirety. For the sake of clarity, some techniques for computing object-level correspondences on images are described herein with reference to
In general terms, computing object-level correspondences reduces a search space for conducting image processing tasks such as, for example, pixel-level correspondence. In one embodiment, instance segmentation is performed to identify different instances of objects in images portraying a scene as viewed from different viewpoints, and instance segmentation maps/masks may be generated in response to the instance segmentation operation. The instance segmentation masks may then be employed for computing object level correspondences.
In one embodiment, object level correspondence allows the matching of a first instance of an object appearing in a first image that depicts a view of a scene from a first viewpoint, to a second instance of the same object appearing in a second image that depicts a view of a scene from a second viewpoint. Once object level correspondence is performed, the search space for performing, for example, pixel-level correspondence, may be limited to the regions of the image that correspond to the same object. Reducing the search space in this manner may result in faster processing of pixel-level correspondence and other similar tasks.
The process starts, and at block 400, the pose estimator 100 receives multi-view images from the main and support cameras 10, 30. A first image captured by one of the cameras may depict one or more objects in a scene from a first viewpoint, and a second image captured by a second camera may depict the one or more objects in the scene from a second viewpoint different from the first viewpoint. The images captured by the cameras may be, for example, polarized images and/or images that have not undergone any polarization filtering.
At block 402 the pose estimator 100 performs instance segmentation and mask generation based on the captured images. In this regard, the pose estimator 100 classifies various regions (e.g. pixels) of an image captured by a particular camera 10, 30 as belonging to particular classes of objects. Each of the different instances of the objects in the image may also be identified, and unique labels be applied to each of the different instances of objects, such as by separately labeling each object in the image with a different identifier.
In one embodiment, segmentation masks delineating the various object instances are also be generated. Each segmentation mask may be a 2-D image having the same dimensions as the input image, where the value of each pixel may correspond to a label (e.g. a particular instance of the object depicted by the pixel). A different segmentation mask may be generated for different images depicting different viewpoints of the objects of interest. For example, a first segmentation mask may be generated to depict object instances in a first image captured by a first camera, and a second segmentation mask may be generated to depict object instances in a second image captured by a second camera. As convolutional neural network such as, for example, Mask R-CNN, may be employed for generating the segmentation masks.
At block 404, the pose estimator 100 engages in object-level correspondence of the objects identified in the segmentation masks. In this regard, the pose estimator may invoke a matching algorithm to identify a segmented instance of a particular object in one image as corresponding (or matching) a segmented instance of the same object in another image. The matching algorithm may be constrained to search for matching object instances along an epipolar line through an object instance in one image to find a corresponding object instance in a different image. In one embodiment, the matching algorithm compares different features of the regions corresponding to the segmented object instances to estimate the object correspondence. The matching of object instances from one image to another may narrow a search space for other image processing tasks such as, for example, performing pixel level correspondence or keypoint correspondence. The search space may be narrowed to the identified regions of the images that are identified as corresponding to the same object.
At block 406, the pose estimator 100 generates an output based on the object-level correspondence. The output may be, for example, a measure of disparity or an estimated depth (e.g., distance from the cameras 10, 30) of the object based on the disparity between corresponding instances as depicted in the various images. In one embodiment, the output is a three-dimensional reconstruction of the configuration of the object and a 6-DoF pose of the object, as described in more detail below with respect to
In one embodiment, the deep learning network 412 is configured to generate feature maps based on the input images 410, and employ a region proposal network (RPN) to propose regions of interest from the generated feature maps. The proposals by the CNN backbone may be provided to a box head 414 for performing classification and bounding box regression. In one embodiment, the classification outputs a class label 416 for each of the object instances in the input images 410, and the bounding box regression predicts bounding boxes 418 for the classified objects. In one embodiment, a different class label 416 is provided to each instance of an object.
The proposals by the CNN backbone may also be provided to a mask head 420 for generating instance segmentation masks. The mask head 416 may be implemented as a fully convolutional network (FCN). In one embodiment, the mask head 420 is configured to encode a binary mask for each of the object instances in the input images 410.
At block 430, the matching algorithm identifies features of a first object instance in a first segmentation mask. The identified features for the first object instance may include a shape of the region of the object instance, a feature vector in the region, and/or keypoint predictions in the region. The shape of the region for the first object instance may be represented via a set of points sampled along the contours of the region. Where a feature vector in the region is used as the feature descriptor, the feature vector may be an average deep learning feature vector extracted via a convolutional neural network.
At block 432, the matching algorithm identifies an epipolar line through the first object instance in the first segmentation mask.
At block 434, the matching algorithm identifies one or more second object instances in a second segmentation mask that may correspond to the first object instance. A search for the second object instances may be constrained to the epipolar line between the first segmentation map and the second segmentation map that runs through the first object instance. In one embodiment, the matching algorithm searches approximately along the identified epiploar line to identify object instances in the second segmentation mask having a same class identifier as the first object instance. For example, if the first object instance belongs to a “dog” class, the matching algorithm evaluates object instances in the second segmentation mask that also belong to the “dog” class, and ignores objects that belong to a different class (e.g., a “cat” class).
At block 436, the matching algorithm identifies the features of the second object instances that belong the same class. As with the first object instance, the features of a particular second object instance may include a shape of the region of the second object instance, a feature vector representing the region, and/or keypoint predictions in the region.
At block 438, the matching algorithm compares the features of the first object instance to the features of second object instances for determining a match. In one embodiment, the matching algorithm identifies a fit between the features of the first object instance and features of the second object instances for selecting a best fit. In one embodiment, the best fit may be identified via a matching function such as the Hungarian matching function. In one embodiment, the features of the object instances are represented as probability distributions, and the matching function attempts to find a match of the probability distributions that minimizes a Kullback-Leibler (KL) divergence.
At block 440, a determination is made as to whether a match has been found. If the answer is YES, an output is generated at block 442. The output may include, for example, information (e.g. object ID) of the second object instance that matched the first object instance.
If the answer is NO, an output may be generate indicating a match failure at block 444.
Accordingly, object level correspondences can be computed from the multi-viewpoint images. These object level correspondences may be used to extract corresponding crops or patches from the multi-viewpoint images, where each of these crops or patches depicts a single instance of an object, and collections of corresponding crops or patches depict the same instance of an object from multiple viewpoints.
In operation 350, the pose estimator 100 loads a 3-D model of the object based on the detected object type one or more object detected in the scene (e.g., for each detected instance of a type of object). For example, in a circumstance where the collection of objects 22 includes a mixture of different types of flexible printed circuit boards, the process of computing object-level correspondences assigns both an instance identifier and a type (or classification) to each detected instance of a flexible printed circuit board (e.g., which of the different types of printed circuit boards). Therefore, a 3-D model of the object may then be loaded from a library based on the detected object type.
In operation 370, the pose estimator 100 aligns the corresponding 3-D model to the appearances of the object to be consistent with the appearance of the object as seen from the one or more viewpoints. In the case of deformable objects, the alignment process in operation 370 may also include deforming the 3-D model to match the estimated configuration of the actual object in the scene. This alignment of the 3-D model provides the 6-DoF pose of the object in a global coordinate system (e.g., a coordinate system based on the main camera 10 or based on the robot controller 28). Details of aspects of the present disclosure for performing the alignment of a 3-D model with the appearance of an object will be described in more detail below.
Aligning Poses and Object Configurations Based on Dense Correspondences
Generally, the methods described herein will make use of a 3-D model or computer-aided-design (CAD) model C of the object (e.g., as loaded in operation 350) and observed two-dimensional (2-D) image data I of the object (e.g., as captured by the cameras in operation 310 and with object-level corresponding patches of the images extracted therefrom in operation 330). In some embodiments, the output of the 6-DoF pose estimation technique (computed by the pose estimator 100) includes a mesh M and its 6-DoF pose a global coordinate system (e.g., 3 dimensional translational and rotational coordinates in the coordinate system used by the controller 28 of a robotic arm 24 or a coordinate system oriented with respect to a master camera 10) for each of the detected objects in the scene. In some embodiments, feature vectors computed by embodiments of the preset disclosure (e.g., prior to a computation of a pose estimate) are supplied as inputs to other layers of a neural network that is trained (end-to-end) to control a system (e.g., a robotic arm) based on input images.
In operation 510, the pose estimator 100 computes an initial pose estimate of an object based on one or more images of the object, such as the image patches extracted in operation 330. The pose estimator 100 may also receive one or more 3-D models corresponding to the detected objects (e.g., as loaded in operation 350) where the 3-D model is posed (e.g., translated and rotated) based on the initial pose estimate. In some embodiments, the initial pose estimate is computed based on detecting keypoints in the one or more images of the object and using a Perspective-n-Point algorithm to match the detected keypoints with corresponding known locations of keypoints in the 3-D model. See, e.g., Zhao, Wanqing, et al. “Learning deep network for detecting 3D object keypoints and 6D poses.” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020. and Lepetit, Vincent, Francesc Moreno-Noguer, and Pascal Fua. “EPnP: An accurate O(n) solution to the PnP problem.” International Journal of Computer Vision 81.2 (2009): 155. The keypoints may be detected using, for example, a classical keypoint detector (e.g., scale-invariant feature transform (SIFT), speeded up robust features (SURF), gradient location and orientation histogram (GLOH), histogram of oriented gradients (HOG), basis coefficients, Haar wavelet coefficients, and the like.) or a trained deep learning keypoint detector such as a trained convolutional neural network using HRNet (Wang, Jingdong, et al. “Deep high-resolution representation learning for visual recognition.” IEEE transactions on pattern analysis and machine intelligence (2020).) with a differential spatial to numerical (DSNT) layer and Blind Perspective-n-Point (Campbell, Dylan, Liu, and Stephen Gould. “Solving the Blind Perspective-n-Point Problem End-To-End With Robust Differentiable Geometric Optimization.” European Conference on Computer Vision. Springer, Cham, 2020.).
As another example, the initial pose estimate may be computed by capturing a depth image or depth map of the object (e.g., using a stereo depth camera or time of flight depth camera) and applying an iterative closest point (ICP) algorithm or a point pair feature matching algorithm (see, e.g., Drost, Bertram, et al. “Model globally, match locally: Efficient and robust 3D object recognition.” 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE, 2010.) to align the 3-D model to the shape of the object as it appears in the depth image. In some embodiments, the initial pose estimate is computed directly from a trained network (see, e.g., Xiang, Yu, et al. “PoseCNN: A convolutional neural network for 6D object pose estimation in cluttered scenes.” arXiv preprint arXiv: 1711.00199 (2017).) and/or approaches such as a dense pose object detector (Zakharov, Sergey, Ivan Shugurov, and Slobodan Ilic. “DPOD: 6D Pose Object Detector and Refiner.” 2019 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE Computer Society, 2019.)
Referring back to
In some embodiments of the present disclosure, the rendered image of the object is a rendered surface normals map, where each pixel or point in the rendered surface normals map is a vector indicating the direction of the surface of the 3-D model depicted at that pixel or point (e.g., a vector perpendicular to the surface of the object at that pixel or point). In some cases, the normal vector at each pixel is encoded in the color channels of an image (e.g., in red, green, and blue color channels). In some embodiments, the pose estimator 100 renders the rendered surface normals map by computing a depth map from the perspective or viewpoint of the observing camera used to capture the observed image (e.g., using the Moller-Trumbore ray-triangle intersection algorithm as described in Möller, Tomas, and Ben Trumbore. “Fast, minimum storage ray-triangle intersection.” Journal of graphics tools 2.1 (1997): 21-28.). According to these embodiments, the depth map of the object is converted to a point cloud, and a rendered surface normals map is computed from the point map (e.g., by computing the slope between neighboring or adjacent points of the point cloud).
In some embodiments of the present disclosure, the pose estimator 100 renders the rendered surface normals map directly from 3-D model with a virtual camera placed at the perspective or viewpoint of the observing camera. This direct rendering may be performed by tracing rays directly from the virtual camera into a virtual scene containing the 3-D model in its initial estimated pose and computing the surface normal of the first surface that each ray intersects with (in particular, the surfaces of the 3-D model in the initial estimated pose that the rays intersect with).
While the rendered image 731 in the embodiments described above include one or more rendered surface normals maps, embodiments of the present disclosure are not limited thereto and the renderer may be configured to generate different types of rendered 2-D images such as color (e.g., red, green, blue) images, monochrome images, and the like.
In operation 570, the pose estimator 100 computes dense image-to-object correspondences between the one or more images of the object and the 3-D model of the object. For example, the rendered image 731 of the object in the scene based on the initial estimated pose and observed image 732 of the object in the same scene (or multiple rendered images 731 and multiple observed images 732 from different viewpoints) are supplied to correspondence calculator 730, which computes dense correspondence features between the rendered image 731 and the observed image 732 (or the rendered images 731 and the corresponding observed images 732 of the object in the scene).
In various embodiments, the correspondence calculator 730 may use different techniques to compute dense correspondence features between the rendered image 731 and the observed image 732. In some embodiments, a disparity neural network is used to detect correspondences (see, e.g., Xu, Haofei, and Juyong Zhang. “AANet: Adaptive aggregation network for efficient stereo matching.” Proceedings of the IEEE/C VF Conference on Computer Vision and Pattern Recognition. 2020.), where the disparity neural network is modified to match pixels along the y-axis of the images (e.g., perpendicular to the usual direction of identifying correspondences by a disparity neural network) in addition to along the x-axis of the input images (as traditional, where the input images are rectified to extend along the x-axis between stereo pairs of images), where the modification may include flattening the output of the neural network before supplying the output to the loss function used to train the disparity neural network, such that the loss function accounts identifies and detects disparities along both the x-axis and the y-axis. In some embodiments, an optical flow neural network is trained and/or retrained to operate on the given types of input data (e.g., observed surface normals maps and observed images), where examples of optical flow neural networks are described in Dosovitskiy, Alexey, et al. “FlowNet: Learning optical flow with convolutional networks.” Proceedings of the IEEE international conference on computer vision. 2015. IIg, Eddy, et al. “FlowNet 2.0: Evolution of optical flow estimation with deep networks.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2017. and Trabelsi, Ameni, et al. “A Pose Proposal and Refinement Network for Better 6D Object Pose Estimation.” Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 2021. In some embodiments, classical techniques for computing dense correspondences are be used, such as classical algorithms for computing optical flow (see, e.g., Horn and Schunck, referenced above) or classical techniques for computing disparity (e.g., block matching, but applied along both the x-axis and y-axis). Other embodiments of the present disclosure include modifications and/or retraining of existing neural network backbones to take two inputs (e.g., the observed image and the rendered image) to compute correspondences.
The observed image or observed images 732 supplied as input to the correspondence calculator 730 may be the same images that were used to compute the initial pose estimate or may be different images, such as images from different viewpoints from those used to compute the initial pose estimate, images captured in different modalities (e.g., polarization and/or different spectra), or images or feature maps computed based on captured or observed images (e.g., observed features in polarization representation spaces or observed surface normals computed from polarization features using shape-from-polarization techniques). Examples of types of images include color images (e.g., red, green, blue images) captured by color cameras, monochrome images (e.g., in the visible light, infrared, or ultraviolet portions of the spectrum), polarization raw frames (e.g., color or monochrome images captured through a polarization filter), polarization features in polarization representation spaces (e.g., angle of linear polarization (AOLP) and degree of linear polarization (DOLP)). As discussed in more detail above, shape from polarization (SfP) provides techniques for computing observed surface normals maps from captured or observed polarization raw frames.
Accordingly, the correspondence calculator 730 computes dense correspondences between the rendered image 731 and the observed image 732.
Through the rendering process, the pose estimator 100 also stores information associated with the rendered image 731 regarding the point in the 3-D model that is represented by each pixel in the rendered image. For example, when rendering the image using a ray tracing technique, each pixel of the rendered image corresponds to a location on the surface of the 3-D model (e.g., in uv coordinate space representing points on the surface of the 3-D model) as defined by a ray connecting the camera origin, the pixel, and the location on the surface of the 3-D model, as modified by any virtual optics system (e.g., as defined by camera intrinsic parameters). As such, the pose estimator 100 stores 2-D to 3-D correspondences between the 2-D rendered image 731 and the 3-D model in its initial pose.
Therefore, the correspondence calculator 730 further computes dense image-to-object correspondences 740 that maps pixels in the observed image 732 to locations on the surface of the 3-D model 711. In more detail, as shown in
In operation 590, the pose estimator 100 updates the estimated pose based on the dense image-to-object correspondences. For example, as shown in
where K is the camera intrinsic matrix of the camera used to capture the observed image of the object, P is a pose matrix representing the transformation between the object and the camera, f :N2→R3 is the dense image-to-object correspondences described above (computed in operation 570) mapping from pixel coordinates in the observed image to 3-D coordinates on the surface of the 3-D model, and X is the domain of f (e.g., across all of the pixels in the observed image of the object).
Because the correspondence calculator 730 computes a large number of correspondences (e.g., dense correspondences) between the image and the 3-D model of the object, these correspondences can also be used to estimate the configuration of the deformable object using a PnP algorithm, thereby enabling the measurement of the configuration of deformable objects (e.g., bags holding loose items such as food, clothes, flexible printed circuit boards, and the like) by deforming the 3-D model to match the configuration of the object. In some embodiments, the deformation of the 3-D model to match the configuration of the deformable object in the images can be computed for every pixel coordinate x ∈ X (where X represents the collection of all pixels in the observed images) as:
{Pf(x)−ProjL(x)(Pf(x))|x ∈0 X}
where L(x) represents a line of a projection of point x from the camera, P is a pose matrix representing the transformation between the object and the camera, f: N2→R3 is the dense image-to-object correspondences described above (computed in operation 570) mapping from pixel coordinates in the observed image to 3-D coordinates on the surface of the 3-D model, projL(x)(Pf (x)) is the estimated depth of the object coordinate seen at point x from the camera along line L(x), and X is the domain of f (e.g., across all of the pixels in the observed image of the object). Accordingly, the above expression provides one estimate of the deformation of the object, e.g., the difference between the predicted location based on the current pose P and a 3-D model of the object (as represented by the term Pf(x)) and the actual observed location of the corresponding point in the observed image, as represented by the term projL(x)(Pf(x)), where the difference represents the change in 3-D coordinates to be applied to make the shape of the 3-D model match up with the actual deformed shape or configuration of the observed object.
In some embodiments where a depth map D of the scene is available (e.g., by capturing a depth map of the scene using a depth camera such as a stereo camera) among the one or more observed images 732, the depth map is used to convert the pixel coordinates x to 3-D coordinates D(x) and therefore the deformation would be computed for each pixel x as:
{Pf(x)−D(x)|I x ∈ X}
Accordingly, the above expression provides one estimate of the deformation of the object, e.g., the difference between the predicted location based on the current pose P and a 3-D model of the object (as represented by the term Pf(x)) and the actual observed location of the corresponding point in the observed depth image D(x), where the difference represents the change in 3-D coordinates to be applied to make the shape of the 3-D model match up with the actual deformed shape or configuration of the observed object.
While
In addition, while
This multi-view joint optimization approach further constrains the search space and increases the accuracy of the pose estimation, as portions of the object that were occluded (e.g., self-occluded) may be visible from the different viewpoints.
As noted above, the discussion of systems and methods for estimating the pose of an object was described in the context of computing a single pose estimate of a single object and/or a pose estimate and configuration of a single deformable object in a scene. However, embodiments are not limited thereto and, instead, include techniques for concurrently or simultaneously estimating the poses of multiple objects in a scene, such as where objects are depicted in a same set of one or more observed images of the scene. The objects may be homogeneous (e.g., all of the same class representable by a same 3-D model) or heterogeneous (e.g., of two or more different object classes that are represented by different 3-D models).
In more detail, in some embodiments, the correspondence calculator 730 is configured (or trained, in the case of neural network) to process an entire camera image in one pass, as opposed to processing a segmented patch of each object in the scene. As such, the runtime of the correspondence calculator 730 is constant with respect to the number of object poses to refine, thereby enabling the efficient detection of object poses, even in cluttered scenes (e.g., with many visible objects).
The large number of correspondences and multiple viewpoints may be used to perform filtering or smoothing to improve the accuracy of the dense image-to-object correspondences. In some embodiments, the filtering is performed by checking the consistency of the point correspondences such as by confirming that corresponding points between different images are projected to approximately the same location on the surface of the 3-D model, and where projected points that are farther from other projected points (e.g., not clustered with the other projected points) may be discarded as inaccurately located outliers or errors.
While some embodiments of the present disclosure are described above as computing 6-DoF poses of objects that may be supplied to a controller, such as for a robotic arm, other embodiments of the preset disclosure include controller pipelines including an optical flow calculator computing optical flow between an observed image and a rendered image of a 3-D model in a current estimated pose to compute dense correspondences, where the dense correspondences are supplied as feature vectors or feature maps within the controller pipeline, without the explicit computation of a 6-DoF pose within the controller pipeline. Such a controller pipeline may include one or more neural networks or sub-networks, where the controller pipeline is trained in an end-to-end fashion based on training data including images of a scene and labels identifying the desired output of the controller, such as a particular destination pose for the end effector of a robotic arm.
Optical flow refinement performs a task of matching parts of the object (finding correspondences) between two different images, such as by using a neural network to solve this correspondence problem. The output of this optical flow operation is then passed to an optimizer to compute the actual pose of the object. This method has several distinct advantages over comparative approaches.
Firstly, many existing 6-DoF pose estimation methods suffer from the problem of symmetries in the target objects. When an object looks the same from multiple viewpoints, it is ambiguous as to which pose the object takes by simply looking at the scene. In some embodiments using optical flow refinement, the initial pose estimate is known and is generally within 5 degrees (in rotation) and 0.5 mm (in translation) of the actual pose of the object (the initial pose may be calculated using techniques described in more detail below). Therefore, when rendering an image (e.g., a 2-D image) of the 3-D model in the initial estimated pose of the object, the pose estimation system may assume that the 3-D model of the object is viewed from the correct orientation and therefore the pose estimation system is confident that it is not viewing the other, symmetrical, side of the object. This lack of ambiguity means that the 2-D to 3-D correspondences computed by the optical flow model are on the correct view of the object and therefore are optimized in the correct pose orientation when performing alignment (e.g., using perspective-n-point or PnP algorithms as described above).
Another advantage of optical flow refinement is that it is robust to occlusions. In comparative pose prediction methods, if an object is partially occluded, the pose prediction may fail because the pose prediction method does not have the information from the occluded part of the object. For example, in keypoint based models, if some of the keypoints of the object are occluded, the predicted 2-D location of the keypoint will be inaccurate, which will increase the error in the final pose estimate. In contrast, when using optical flow based refinement according to some embodiments of the present disclosure, the correspondence between the rendered image and the observed image of the object is performed only on the visible (not occluded) parts of the object. As such, the lack of information of occluded parts of the object does not impact the optimization process. Additionally, using optical flow to compute correspondences results in a correspondence map for every visible pixel depicting the object and therefore the PnP algorithm has more than enough information to solve for a refined pose.
The large number of correspondences (e.g., dense correspondences) between pixels of the observed images and coordinates of the 3-D model also means that using optical flow refinement in accordance with embodiments of the present disclosure reduces the impact of errors in individual correspondences. In particular, the large number of correspondences causes the variance of the pose estimation from PnP to be drastically reduced versus comparative techniques (e.g., where a limited number of keypoints are detected at relatively sparse locations on the object). This is especially helpful with deformable objects, as the deformation of these objects tend to generate conflicting information for PnP algorithms, and because the dense correspondence map enables the detection of correspondences across the deformable surface of the object rather than merely at a few sparse keypoints on the surface of the object.
As a result of these features, experimental results on four different small objects (e.g., less than 30 mm in width) showed an average reduction in error rates in translation and rotation by about 40%. In particular, each type of small object was scattered into a homogenous collection of about 20 to 50 parts of the same type, and error rates were determined based on techniques described in International Patent Application PCT/US 20/63044, filed in the United States Patent and Trademark Office on Dec. 3, 2020. In more detail, the average translation and rotation error of a comparative pose estimation system (e.g., a keypoint-based pose estimation pipeline using a convolutional neural network-based keypoint detector) about 0.3 mm and 2.4 degrees, respectively. In contrast, the average translation and rotation error of an embodiment of the present disclosure using dense correspondences based on a disparity network, operating on the same input images of the objects, was about 0.2 mm and 1.5 degrees, while maintaining a low run time (e.g., short cycle time).
While the present invention has been described in connection with certain exemplary embodiments, it is to be understood that the invention is not limited to the disclosed embodiments, but, on the contrary, is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims, and equivalents thereof.
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| Number | Date | Country | |
|---|---|---|---|
| 20220405506 A1 | Dec 2022 | US |
