Patent Application
20150185054
Unless otherwise indicated herein, the materials described in this section are not prior art to the claims in this application and are not admitted to be prior art by inclusion in this section.
Sensor fusion includes combining sensor data or data derived from sensory data from independent sources such that resulting information is more complete. Data sources for a fusion process may include multiple distinct sensors. Each sensor may provide different information about the same object in an environment, or about the same location in an environment, for example. By combining the sensor data, a more complete depiction of the object or location can be provided. As an example, one sensor may include a camera to capture an image of an object, and another sensor may include location detection capabilities to determine a location of a device used to capture the image. By combining the sensor data, specific location information for the image data is provided.
In one example, a method performed by a device having an application processor configured to function based on an operating system and a co-processor configured to receive data from a plurality of sensors of the device is provided. The method comprises determining an interrupt by a given sensor of the plurality of sensors of the device, and the interrupt is indicative of the given sensor having data for output. The method also comprises providing, by the co-processor, a timestamp of the interrupt by the given sensor that is indicative of a time that the given sensor has data for output, and receiving, by the co-processor, the data for output from the given sensor. The method also comprises associating the timestamp of the interrupt by the given sensor with the received data from the given sensor, associating together data received from the plurality of sensors into data structures based on timestamps of the data as provided by the co-processor, and providing, by the co-processor, the data structures to the application processor in sequence based on the timestamps of the data.
In another example, a computer readable memory configured to store instructions that, when executed by a device having an application processor configured to function based on an operating system and a co-processor configured to receive data from a plurality of sensors of the device, cause the device to perform functions is provided. The functions comprise determining an interrupt by a given sensor of the plurality of sensors of the device, and the interrupt is indicative of the given sensor having data for output. The functions also comprise providing, by the co-processor, a timestamp of the interrupt by the given sensor that is indicative of a time that the given sensor has data for output, and receiving, by the co-processor, the data for output from the given sensor. The functions also comprise associating the timestamp of the interrupt by the given sensor with the received data from the given sensor, associating together data received from the plurality of sensors into data structures based on timestamps of the data as provided by the co-processor, and providing, by the co-processor, the data structures to the application processor in sequence based on the timestamps of the data.
In another example, a device is provided that comprises an application processor configured to function based on an operating system, a plurality of sensors, and a co-processor configured to receive data from the plurality of sensors and configured to perform functions. The functions comprise determine an interrupt by a given sensor of the plurality of sensors, and the interrupt is indicative of the given sensor having data for output. The functions also comprise provide a timestamp of the interrupt by the given sensor that is indicative of a time that the given sensor has data for output, receive the data for output from the given sensor, associate the timestamp of the interrupt by the given sensor with the received data from the given sensor, associate together data received from the plurality of sensors into data structures based on timestamps of the data as provided by the co-processor, and provide the data structures to the application processor in sequence based on the timestamps of the data.
These as well as other aspects, advantages, and alternatives, will become apparent to those of ordinary skill in the art by reading the following detailed description, with reference where appropriate to the accompanying figures.
The following detailed description describes various features and functions of the disclosed systems and methods with reference to the accompanying figures. In the figures, similar symbols identify similar components, unless context dictates otherwise. The illustrative system and method embodiments described herein are not meant to be limiting. It may be readily understood that certain aspects of the disclosed systems and methods can be arranged and combined in a wide variety of different configurations, all of which are contemplated herein.
Within examples, methods and systems for synchronizing data received from multiple sensors of a device are described. Example methods may be performed by a device having an application processor configured to function based on an operating system and a co-processor configured to receive data from a plurality of sensors of the device. An example method includes determining an interrupt by a given sensor of a plurality of sensors of the device, and the interrupt is indicative of the given sensor having data for output. The method also includes providing, by a co-processor, a timestamp of the interrupt by the given sensor that is indicative of a time that the given sensor has data for output, and receiving, by the co-processor, the data for output from the given sensor. The method also includes associating the timestamp of the interrupt by the given sensor with the received data from the given sensor, and associating together data received from the plurality of sensors into data structures based on timestamps of the data as provided by the co-processor. The method also includes providing, by the co-processor, the data structures to the application processor in sequence based on the timestamps of the data.
In some existing devices, sensor data is provided to an application processor and the sensor data receives a timestamp from software executed by the application processor. However, some time elapses between data collection and data timestamp by the software due to transmission of the data over a communication bus between the sensor and the application processor or due to buffering or queuing of data, and thus, timestamps may be inaccurate or not synchronized across all data received from multiple sensors. Within examples presented herein, the co-processor may be configured to provide hardware based timestamps for all sensor data of the device to capture accurate and precise timestamps for data collected or measured by the sensors.
Referring now to the figures,
The computing device 100 may include an interface 102, a wireless communication component 104, a cellular radio communication component 106, a global position system (GPS) receiver 108, sensor(s) 110, data storage 112, and processor(s) 114. Components illustrated in
The interface 102 may be configured to allow the computing device 100 to communicate with other computing devices (not shown), such as a server. Thus, the interface 102 may be configured to receive input data from one or more computing devices, and may also be configured to send output data to the one or more computing devices. The interface 102 may be configured to function according to a wired or wireless communication protocol. In some examples, the interface 102 may include buttons, a keyboard, a touchscreen, speaker(s) 118, microphone(s) 120, and/or any other elements for receiving inputs, as well as one or more displays, and/or any other elements for communicating outputs.
The wireless communication component 104 may be a communication interface that is configured to facilitate wireless data communication for the computing device 100 according to one or more wireless communication standards. For example, the wireless communication component 104 may include a Wi-Fi communication component that is configured to facilitate wireless data communication according to one or more IEEE 802.11 standards. As another example, the wireless communication component 104 may include a Bluetooth communication component that is configured to facilitate wireless data communication according to one or more Bluetooth standards. Other examples are also possible.
The cellular radio communication component 106 may be a communication interface that is configured to facilitate wireless communication (voice and/or data) with a cellular wireless base station to provide mobile connectivity to a network. The cellular radio communication component 106 may be configured to connect to a base station of a cell in which the computing device 100 is located, for example.
The GPS receiver 108 may be configured to estimate a location of the computing device 100 by precisely timing signals sent by GPS satellites.
The sensor(s) 110 may include one or more sensors, or may represent one or more sensors included within the computing device 100. Example sensors include an accelerometer, gyroscope, pedometer, light sensors, microphone, camera(s), infrared flash, barometer, magnetometer, GPS, WiFi, near field communication (NFC), Bluetooth, projector, depth sensor, temperature sensors, or other location and/or context-aware sensors.
The data storage 112 may store program logic 122 that can be accessed and executed by the processor(s) 114. The data storage 112 may also store data collected by the sensor(s) 110, or data collected by any of the wireless communication component 104, the cellular radio communication component 106, and the GPS receiver 108.
The processor(s) 114 may be configured to receive data collected by any of sensor(s) 110 and perform any number of functions based on the data. As an example, the processor(s) 114 may be configured to determine one or more geographical location estimates of the computing device 100 using one or more location-determination components, such as the wireless communication component 104, the cellular radio communication component 106, or the GPS receiver 108. The processor(s) 114 may use a location-determination algorithm to determine a location of the computing device 100 based on a presence and/or location of one or more known wireless access points within a wireless range of the computing device 100. In one example, the wireless location component 104 may determine the identity of one or more wireless access points (e.g., a MAC address) and measure an intensity of signals received (e.g., received signal strength indication) from each of the one or more wireless access points. The received signal strength indication (RSSI) from each unique wireless access point may be used to determine a distance from each wireless access point. The distances may then be compared to a database that stores information regarding where each unique wireless access point is located. Based on the distance from each wireless access point, and the known location of each of the wireless access points, a location estimate of the computing device 100 may be determined.
In another instance, the processor(s) 114 may use a location-determination algorithm to determine a location of the computing device 100 based on nearby cellular base stations. For example, the cellular radio communication component 106 may be configured to identify a cell from which the computing device 100 is receiving, or last received, signal from a cellular network. The cellular radio communication component 106 may also be configured to measure a round trip time (RTT) to a base station providing the signal, and combine this information with the identified cell to determine a location estimate. In another example, the cellular communication component 106 may be configured to use observed time difference of arrival (OTDOA) from three or more base stations to estimate the location of the computing device 100.
In some implementations, the computing device 100 may include a device platform (not shown), which may be configured as a multi-layered Linux platform. The device platform may include different applications and an application framework, as well as various kernels, libraries, and runtime entities. In other examples, other formats or operating systems may operate the computing g device 100 as well.
The communication link 116 is illustrated as a wired connection; however, wireless connections may also be used. For example, the communication link 116 may be a wired serial bus such as a universal serial bus or a parallel bus, or a wireless connection using, e.g., short-range wireless radio technology, or communication protocols described in IEEE 802.11 (including any IEEE 802.11 revisions), among other possibilities.
The computing device 100 may include more or fewer components. Further, example methods described herein may be performed individually by components of the computing device 100, or in combination by one or all of the components of the computing device 100.
The IMU 202 may be configured to determine a velocity, orientation, and gravitational forces of the computing device 200 based on outputs of the gyroscope 204 and the accelerometer 206.
The GS camera 208 may be configured on the computing device 200 to be a rear facing camera, so as to face away from a front of the computing device 200. The GS camera 208 may be configured to read outputs of all pixels of the camera 208 simultaneously. The GS camera 208 may be configured to have about a 120-170 degree field of view, such as a fish eye sensor, for wide-angle viewing.
The RS camera 210 may be configured to read outputs of pixels from a top of the pixel display to a bottom of the pixel display. As one example, the RS camera 210 may be a red/green/blue (RGB) infrared (IR) 4 megapixel image sensor, although other sensors are possible as well. The RS camera 210 may have a fast exposure so as to operate with a minimum readout time of about 5.5 ms, for example. Like the GS camera 208, the RS camera 210 may be a rear facing camera.
The camera 212 may be an additional camera in the computing device 200 that is configured as a front facing camera, or in a direction facing opposite of the GS camera 208 and the RS camera 210. The camera 212 may be configured to capture images of a first viewpoint of the computing device 200 and the GS camera 208 and the RS camera 210 may be configured to capture images of a second viewpoint of the device that is opposite the first viewpoint. The camera 212 may be a wide angle camera, and may have about a 120-170 degree field of view for wide angle viewing, for example.
The IR flash 214 may provide a light source for the computing device 200, and may be configured to output light in a direction toward a rear of the computing device 200 so as to provide light for the GS camera 208 and RS camera 210, for example. In some examples, the IR flash 214 may be configured to flash at a low duty cycle, such as 5 Hz, or in a non-continuous manner as directed by the co-processor 230 or application processor 232. The IR flash 214 may include an LED light source configured for use in mobile devices, for example.
Referring back to
The magnetometer 218 may be configured to provide roll, yaw, and pitch measurements of the computing device 200, and can be configured to operate as an internal compass, for example. In some examples, the magnetometer 218 may be a component of the IMU 202 (not shown).
The GPS receiver 220 may be similar to the GPS receiver 108 described in the computing device 100 of
The WiFi/NFC/Bluetooth sensor 222 may include wireless communication components configured to operate according to WiFi and Bluetooth standards, as discussed above with the computing device 100 of
The projector 224 may be or include a structured light projector that has a laser with a pattern generator to produce a dot pattern in an environment. The projector 224 may be configured to operate in conjunction with the RS camera 210 to recover information regarding depth of objects in the environment, such as three-dimensional (3D) characteristics of the objects. For example, the separate depth sensor 226 may be configured to capture video data of the dot pattern in 3D under ambient light conditions to sense a range of objects in the environment. The projector 224 and/or depth sensor 226 may be configured to determine shapes of objects based on the projected dot pattern. By way of example, the depth sensor 226 may be configured to cause the projector 224 to produce a dot pattern and cause the RS camera 210 to capture an image of the dot pattern. The depth sensor 226 may then process the image of the dot pattern, use various algorithms to triangulate and extract 3D data, and output a depth image to the co-processor 230.
The temperature sensor 228 may be configured to measure a temperature or temperature gradient, such as a change in temperature, for example, of an ambient environment of the computing device 200.
The co-processor 230 may be configured to control all sensors on the computing device 200. In examples, the co-processor 230 may control exposure times of any of cameras 208, 210, and 212 to match the IR flash 214, control the projector 224 pulse sync, duration, and intensity, and in general, control data capture or collection times of the sensors. The co-processor 230 may also be configured to process data from any of the sensors into an appropriate format for the application processor 232. In some examples, the co-processor 230 merges all data from any of the sensors that corresponds to a same timestamp or data collection time (or time period) into a single data structure to be provided to the application processor 232.
The application processor 232 may be configured to control other functionality of the computing device 200, such as to control the computing device 200 to operate according to an operating system or any number of software applications stored on the computing device 200. The application processor 232 may use the data collected by the sensors and received from the co-processor to perform any number of types of functionality. The application processor 232 may receive outputs of the co-processor 230, and in some examples, the application processor 232 may receive raw data outputs from other sensors as well, including the GS camera 208 and the RS camera 210.
The second IMU 234 may output collected data directly to the application processor 232, which may be received by the application processor 232 and used to trigger other sensors to begin collecting data. As an example, outputs of the second IMU 234 may be indicative of motion of the computing device 200, and when the computing device 200 is in motion, it may be desired to collect image data, GPS data, etc. Thus, the application processor 232 can trigger other sensors through communication signaling on common buses to collect data at the times at which the outputs of the IMU 234 indicate motion.
The computing device 200 shown in
In addition, for the method 400 and other processes and methods disclosed herein, the flowchart shows functionality and operation of one possible implementation of present embodiments. In this regard, each block may represent a module, a segment, or a portion of program code, which includes one or more instructions executable by a processor for implementing specific logical functions or steps in the process. The program code may be stored on any type of computer readable medium, for example, such as a storage device including a disk or hard drive. The computer readable medium may include a non-transitory computer readable medium, for example, such as computer-readable media that stores data for short periods of time like register memory, processor cache and Random Access Memory (RAM). The computer readable medium may also include other non-transitory media, such as secondary or persistent long term storage, like read only memory (ROM), optical or magnetic disks, compact-disc read only memory (CD-ROM), for example. The computer readable media may also be any other volatile or non-volatile storage systems. The computer readable medium may be considered a computer readable storage medium, a tangible storage device, or other article of manufacture, for example. The program code (or data for the code) may also be stored or provided on other media including communication media, such as a wireless communication media, for example.
In addition, for the method 400 and other processes and methods disclosed herein, each block in
Functions of the method 400 may be fully performed by a computing device, or may be distributed across multiple computing devices and/or a server. In some examples, the computing device may receive information from sensors of the computing device, or where the computing device is a server the information can be received from another device that collects the information. The computing device could further communicate with a server to receive information from sensors of other devices, for example. The method 400 may further be performed by a device that has an application processor configured to function based on an operating system and a co-processor configured to receive data from a plurality of sensors of the device. The sensors may include any sensors as described above in any of
At block 402, the method 400 includes determining an interrupt by a given sensor of the plurality of sensors of the device. The interrupt is indicative of the given sensor having data for output. As an example, the co-processor may receive a signal from the sensor indicating the interrupt, such as a signal including a flag set to true. The sensor may send the signal once the sensor has data for output, for example, after performing a sensing function. A sensor may have data for output based on the sensor performing data collection internally, or based on being triggered to perform a data collection.
At block 404, the method 400 includes providing, by the co-processor, a timestamp of the interrupt by the given sensor that is indicative of a time that the given sensor has data for output. For instance, at the time the co-processor determines the interrupt, the co-processor generates a timestamp for the interrupt, and stores the timestamp with the interrupt, and with other metadata indicating the sensor providing the interrupt. In this way, the co-processor marks an exact time at which the sensor collected data.
As another example, for cameras, an image capture may not occur at a discrete point in time due to image capture requiring time to expose an image. It may be desired to determine a timestamp of the image that corresponds to a beginning, middle, or ending of the exposure. For an RS camera, for example, which exposes from top to bottom, a top portion of the image occurs earlier in time than a bottom portion of the image, and the image may be associated with only one timestamp, and so the co-processor can generate a timestamp at a center exposure time to minimize a time difference between a top and bottom portion of the image. The co-processor may know an exposure time of the camera and can generate the timestamp to be at a center exposure time based on an average of exposure times of the camera once triggered to capture an image, for example.
At block 406, the method 400 includes receiving, by the co-processor, the data for output from the given sensor. Thus, after generating the timestamp, the co-processor can retrieve the data from the sensor. The data may be provided from the sensor to the co-processor along any number of communication buses within the computing device. The data transmission can occur over a time period, e.g., about 10-30 ms. Thus, the co-processor can request the data from the sensor that corresponds to the interrupt. Timestamping and data retrieval are performed separately because a query for sensor data may be slow and take time, and accurate timestamps may be required for data processing. Thus, the interrupt is used as a flag to generate the timestamp, and then a data fetch is subsequently performed.
At block 408, the method 400 includes associating the timestamp of the interrupt by the given sensor with the received data from the given sensor. In this manner, the co-processor can associate a time at which the data was collected by the sensor with the data, rather than associating a time of receipt of the data at the co-processor as the data collection time. Since the data transmission from the sensor to the co-processor consumes some time, an exact timestamp at time of data collection by the sensor is provided based on the timestamp of the previously received interrupt.
At block 410, the method 400 includes associating together data received from the plurality of sensors into data structures based on timestamps of the data as provided by the co-processor. For example, the co-processor may receive data from many sensors of the device, and the data can be organized based on timestamps generated for interrupts as provided by the co-processor. For data received by the co-processor from sensors and associated with an interrupt timestamp of the same time, such data can be associated together or formatted together into one data structure.
The data can be formatted into any number of types of data structures. In one example, the data may be stored into one file indicating a type of data, a sensor that collected the data, and a timestamp for the data. In another example, the data can be formatted into an image frame format data structure, where data is stored as pixel values for the given timestamp. The image frame format may take the form of a transport datatype of “RGB” or “YUV” for traditional transmission across a camera interface.
At block 412, the method 400 includes providing, by the co-processor, the data structures to the application processor in sequence based on the timestamps of the data. The application processor may then receive the data in sequence as the data is collected.
Within examples, using the method 400 in
In some examples, the method 400 may further include providing, by the co-processor, data received from an IMU to the application processor prior to associating together the data received from the plurality of sensors into the data structures, and subsequently providing the data structures to the application processor. An IMU may collect data at a higher frequency than other sensors, and the IMU data may be more useful to the application processor than other data. Thus, the co-processor can generate timestamps for IMU data, retrieve the IMU data, and immediately provide the IMU data to the application processor ahead of other data associated with the same timestamp so that the application processor can have the data to perform time-sensitive functions.
The computing device may include a bus for communication between the application processor and the co-processor, and another bus for communication between the co-processor and the sensors that has a latency less than communication on between the application processor and the co-processor. In some instances, the co-processor may receive data from the IMU and provide the data to the application processor on the same bus so as to provide the data more quickly to the application processor. The bus may be an SPI bus as a fast low-latency communication channel for IMU data. The data structures, which are a larger amount of data, can then be assembled and provided to application processor on the I2C bus, for example, which allows for larger bandwidths of data transmission. The application processor may benefit by receiving the IMU data alone quickly, for example.
In further examples, the device may include multiple cameras the co-processor may provide a trigger signal to a first camera requesting data output from the first camera. The trigger signal can be received by other cameras of the device via the first camera, so as to cause capture of images in synchrony. As examples, one camera may be an RS camera and another camera may be a GS camera. The trigger signal can be provided to the GS camera at about a center exposure time of the RS camera so as to cause exposure of the GS camera at about the center exposure time of the RS camera.
The sensors may also include a red-green-blue (RGB) camera and an infrared (IR) sensor. The IR sensor may be configured to output data at a frequency that is less than an output frequency of the RGB camera. Thus, when associating together data form the sensors, data output by the IR sensor can be interleaved within data output by the RGB camera based on timestamps of the respective data as provided by the co-processor. As an example, about four frames of image data may be received from the camera for every one frame of depth data from the IR sensor.
In further examples, sensors may output data at a first frequency data can be associated together by the co-processor into data structures at a second frequency that is less than the first frequency. For example, data may be output by the sensors faster than a camera framerate used to transmit data, and thus, the co-processor may provide most recent IMU data samples (which may be configured to run at 120 Hz) within the data structure at a slower frequency so as to provide multiple samples of IMU data within one data structure.
In some examples, the sensors may include a buffer to store recently collected data, and the sensors may provide current data collected as well as previous data for output to the co-processor. The previous data may be redundant data received for error correction, since the previous data was likely received previously by the co-processor. For instance, the IMU may have a running buffer of redundant IMU data, and instead of just pulling latest data from the IMU, the co-processor can collect all data from the buffer to receive a most recent X number of samples from the IMU. If a frame of data is ever missed or corrupted during data transmission, the redundant data received by the co-processor received in a next frame of data can be used to fill in missing data. As an example, if the computing device were rotating 30 degrees per second, and a frame of data was missed, an exact location and orientation of the computing device may not be known (i.e., the location/orientation estimate may be now a few degrees off from where the device is actually located). By providing redundant data, lost or missing data from the IMU can be identified to make corrections to any location/orientation estimates of the device.
Example RS exposure and GS exposure times for an RS camera and GS camera are shown along the co-processor time axis as well. Since the RS exposure occurs from a top to a bottom of the image, the exposure is over a longer time period than exposure of the GS camera. The RS camera may operate at a frequency of 30 Hz, for example. A GS camera trigger time is set to coincide with an exposure of the center scanline of the RS camera so that centers of the GS camera and RS camera exposures are time aligned. For example, the RS camera may require 30 ms to capture an image, and the GS camera may only require 20 ms, and thus, the GS trigger time can be set to be 5 ms after the RS exposure time so that the centers of the GS camera and RS camera exposure times are aligned. The trigger time for the GS camera may then be as follows
GS Trigger time=RS exposure start time+½ RS exposure time−½ GS exposure time
The center scanline of the cameras is the timestamp produced for the camera images by the co-processor as determined from a timestamped end of a camera readout and subtracting a deterministic readout and exposure time for the frame, for example.
The co-processor captures data from both cameras and IMU in a synchronized manner, and then combines the data into a data structure to be provided to an application processor. The data structures may be in an image frame format.
Thus, IMU data may be recorded as soon as a packet is ready and time stamped at a frame rate of the IMU (i.e., if the IMU is set at 100 Hz mode, then IMU packets may be read-out at 100 Hz, and corresponding co-processor timestamps on the IMU packets can occur at 100 Hz). The IMU data may then be time-aligned with an image frame in which the IMU data is packed. In examples, a first IMU data packet in a frame corresponds with an image trigger time of the GS camera. In other examples, the table below shows a sample set of images and IMU data packed into frames when the camera is running at 30 Hz (approximated as 33 ms delta t in the table), and the IMU is running at 120 Hz (approximated as 8 ms delta t in the table).
Both the RS camera and the GS camera may be time stamped with respect to the co-processor time axis at a beginning of their respective exposure. Both the RS camera and the GS camera exposure times are also recorded to compute a beginning, middle, and end of exposure.
In further examples, image frames may include additional data as well, such as data from previous frames. As an example, frame 2 may include all data for frame 2 as well as all data within frame 1, and the redundancy of resending data from frame 1 within frame 2 can be used for error correction or data verifications.
Within examples described herein, data output from sensors can be associated with an exact timestamp of the data corresponding to the time the data was collected by the sensors, and data from multiple sensors can be synchronized into data structures. In some examples, this enables synchronization of data indicating motion of the device (e.g., data output by a gyroscope) with camera images. Sensor data from all sensors of a device can be accurately synchronized to take a readout of the sensors and match data from all sensors that was collected at the same time together into a data structure without any software timestamp delays or inaccuracies, for example.
It should be understood that arrangements described herein are for purposes of example only. As such, those skilled in the art will appreciate that other arrangements and other elements (e.g. machines, interfaces, functions, orders, and groupings of functions, etc.) can be used instead, and some elements may be omitted altogether according to the desired results. Further, many of the elements that are described are functional entities that may be implemented as discrete or distributed components or in conjunction with other components, in any suitable combination and location, or other structural elements described as independent structures may be combined.
While various aspects and embodiments have been disclosed herein, other aspects and embodiments will be apparent to those skilled in the art. The various aspects and embodiments disclosed herein are for purposes of illustration and are not intended to be limiting, with the true scope being indicated by the following claims, along with the full scope of equivalents to which such claims are entitled. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting.
