Patent Application
20230305115
This disclosure relates generally to light detection and ranging by a LiDAR system and, more particularly, to effective communication of point cloud data using data encoding and compression.
Light detection and ranging (LiDAR) systems use light pulses to create an image or point cloud of the external environment. A LiDAR system may be a scanning or non-scanning system. Some typical scanning LiDAR systems include a light source, a light transmitter, a light steering system, and a light detector. The light source generates a light beam that is directed by the light steering system in particular directions when being transmitted from the LiDAR system. When a transmitted light beam is scattered or reflected by an object, a portion of the scattered or reflected light returns to the LiDAR system to form a return light pulse. The light detector detects the return light pulse. Using the difference between the time that the return light pulse is detected and the time that a corresponding light pulse in the light beam is transmitted, the LiDAR system can determine the distance to the object based on the speed of light. This technique of determining the distance is referred to as the time-of-flight (ToF) technique. The light steering system can direct light beams along different paths to allow the LiDAR system to scan the surrounding environment and produce images or point clouds. A typical non-scanning LiDAR system illuminate an entire field-of-view (FOV) rather than scanning through the FOV. An example of the non-scanning LiDAR system is a flash LiDAR, which can also use the ToF technique to measure the distance to an object. LiDAR systems can also use techniques other than time-of-flight and scanning to measure the surrounding environment.
Embodiments of a system and method for LiDAR having data encoding and compression are provided. It should be appreciated that the present embodiment can be implemented in numerous ways, such as a process, an apparatus, a system, a device, or a method. Several embodiments are described below.
In some embodiments, the method of data encoding and compression within a LiDAR system is provided. In particular, the method includes steering one or more beams of light using a plurality of reflective facets of the polygon mirror to scan a Field-Of-View (FOV) in a first direction. The method further includes steering the one or more beams of light using a galvanometer mirror to scan the FOV in a second direction. After return light pulses are reflected by one or more objects in the FOV, the method includes receiving detection data from an optical detector of the scanning system, wherein the detection data is associated with the return light pulse. The method includes decoding the detection data into raw data and configuring output data based on the raw data, trigger data, encoder data, and time synchronization data. The output data have a compressed format including a data set for one baseline channel and differential channel data for one or more channels based upon the baseline channel.
In some embodiments, the compressed output data can be sent in blocks, wherein each block corresponds to one laser firing and contains received data points from each one of the one or more channels. In some examples, the output data starts with a block header followed by data points of the point cloud. Representation of the output data may comprise the use of a spherical coordinate system. Further, the method of data encoding and compression within a LiDAR system includes transmitting the trigger data to one or more sources of light to control emission timing of the one or more beams of light. The method also includes receiving the encoder data from the optical steering mechanism based on encoder position data provided by the galvanometer mirror and the polygon mirror. In some embodiments, the output data and the time synchronization data are transmitted to external sources for further data processing.
In some embodiments, the LiDAR system having data encoding and compression is provided. In particular, the LiDAR system includes a laser source that supplies a plurality of light pulses to a optical steering mechanism, wherein the optical steering mechanism directs the plurality of light pulses in accordance with the FOV. In some examples, the optical steering mechanism comprises a mirror motor galvanometer for vertical scanning and a rotatable polygon mirror for horizontal scanning. The LiDAR system further includes an encoder/decoder processing agent coupled to receive detection data to transform the detection data into raw data and configure output data from the raw data, the trigger data, the encoder data, and the time synchronization data. In some embodiments, the output data is in a compressed format including a data set for one baseline channel and differential channel data for one or more channels based upon the baseline channel. Accordingly, the point cloud output data can be sent in blocks, wherein each block corresponds to one laser firing and contains received data points from each one of the one or more channels. In some examples, the output data starts with a block header followed by data points of the point cloud. Representation of point cloud output data may comprise the use of a spherical coordinate system. The system may further include a controller coupled to receive data from the encoder/decoder processing agent. The controller couples to the optical steering mechanism and the laser source to coordinate movement speed of the integrated mirror motor galvanometer and the rotatable polygon mirror with the plurality of light pulses supplied by the laser source.
In some embodiments, a tangible, non-transitory, computer-readable media having instructions whereupon which, when executed by a processor, cause the processor to perform the data encoding and compression method described herein. In particular, the method includes steering one or more beams of light using a plurality of reflective facets of the polygon mirror to scan a Field-Of-View (FOV) in a first direction. The method further includes steering the one or more beams of light using a galvanometer mirror to scan the FOV in a second direction. After return light pulses are reflected by one or more objects in the FOV, the method includes receiving detection data from an optical detector of the scanning system, wherein the detection data is associated with the return light pulses. The method includes decoding the detection data into raw data and configure output data from the raw data, trigger data, encoder data, and time synchronization data. The output data have a compressed format including a data set for one baseline channel and differential channel data for one or more channels based upon the baseline channel. In some embodiments, the point cloud output data can be sent in blocks, wherein each block corresponds to one laser firing and contains received data points from each one of the one or more channels. In some examples, the output data starts with a block header followed by data points of the point cloud. Representation of point cloud output data may comprise the use of a spherical coordinate system. Further, the method of data encoding and compression within a LiDAR system includes transmitting the trigger data to one or more sources of light to control emission timing of the one or more beams of light. The method also includes transmitting the encoder data to the optical steering mechanism for encoding the galvanometer mirror and the polygon mirror.
Other aspects and advantages of the embodiments will become apparent from the following detailed description taken in conjunction with the accompanying drawings which illustrate, by way of example, the principles of the described embodiments.
The present application can be best understood by reference to the embodiments described below taken in conjunction with the accompanying drawing figures, in which like parts may be referred to by like numerals.
To provide a more thorough understanding of various embodiments of the present invention, the following description sets forth numerous specific details, such as specific configurations, parameters, examples, and the like. It should be recognized, however, that such description is not intended as a limitation on the scope of the present invention but is intended to provide a better description of the exemplary embodiments.
Throughout the specification and claims, the following terms take the meanings explicitly associated herein, unless the context clearly dictates otherwise:
The phrase “in one embodiment” as used herein does not necessarily refer to the same embodiment, though it may. Thus, as described below, various embodiments of the disclosure may be readily combined, without departing from the scope or spirit of the invention.
As used herein, the term “or” is an inclusive “or” operator and is equivalent to the term “and/or,” unless the context clearly dictates otherwise.
The term “based on” is not exclusive and allows for being based on additional factors not described unless the context clearly dictates otherwise.
As used herein, and unless the context dictates otherwise, the term “coupled to” is intended to include both direct coupling (in which two elements that are coupled to each other contact each other) and indirect coupling (in which at least one additional element is located between the two elements). Therefore, the terms “coupled to” and “coupled with” are used synonymously. Within the context of a networked environment where two or more components or devices are able to exchange data, the terms “coupled to” and “coupled with” are also used to mean “communicatively coupled with”, possibly via one or more intermediary devices. The components or devices can be optical, mechanical, and/or electrical devices.
Although the following description uses terms “first,” “second,” etc. to describe various elements, these elements should not be limited by the terms. These terms are only used to distinguish one element from another. For example, a first sensor could be termed a second sensor and, similarly, a second sensor could be termed a first sensor, without departing from the scope of the various described examples. The first sensor and the second sensor can both be sensors and, in some cases, can be separate and different sensors. The first direction of steering the one or more beams of light using a plurality of reflective facets of the polygon mirror to scan the FOV could be termed a second direction of steering the one or more beams of light. Additionally, throughout the specification, the meaning of “a”, “an”, and “the” includes plural references, and the meaning of “in” includes “in” and “on.”
Although some of the various embodiments presented herein constitute a single combination of inventive elements, it should be appreciated that the inventive subject matter is considered to include all possible combinations of the disclosed elements. As such, if one embodiment comprises elements A, B, and C, and another embodiment comprises elements B and D, then the inventive subject matter is also considered to include other remaining combinations of A, B, C, or D, even if not explicitly discussed herein. Further, the transitional term “comprising” means to have as parts or members, or to be those parts or members. As used herein, the transitional term “comprising” is inclusive or open-ended and does not exclude additional, unrecited elements or method steps.
As used in the description herein and throughout the claims that follow, when a system, engine, server, device, module, or other computing element is described as being configured to perform or execute functions on data in a memory, the meaning of “configured to” or “programmed to” is defined as one or more processors or cores of the computing element being programmed by a set of software instructions stored in the memory of the computing element to execute the set of functions on target data or data objects stored in the memory.
It should be noted that any language directed to a computer should be read to include any suitable combination of computing devices or network platforms, including servers, interfaces, systems, databases, agents, peers, engines, controllers, modules, or other types of computing devices operating individually or collectively. One should appreciate the computing devices comprise a processor configured to execute software instructions stored on a tangible, non-transitory computer readable storage medium (e.g., hard drive, FPGA, PLA, solid state drive, RAM, flash, ROM, or any other volatile or non-volatile storage devices). The software instructions configure or program the computing device to provide the roles, responsibilities, or other functionality as discussed below with respect to the disclosed apparatus. Further, the disclosed technologies can be embodied as a computer program product that includes a non-transitory computer readable medium storing the software instructions that causes a processor to execute the disclosed steps associated with implementations of computer-based algorithms, processes, methods, or other instructions. In some embodiments, the various servers, systems, databases, or interfaces exchange data using standardized protocols or algorithms, possibly based on HTTP, HTTPS, AES, public-private key exchanges, web service APIs, known financial transaction protocols, or other electronic information exchanging methods. Data exchanges among devices can be conducted over a packet-switched network, the Internet, LAN, WAN, VPN, or other type of packet switched network; a circuit switched network; cell switched network; or other type of network.
Current LiDAR systems use light pulses to create an image or a 3D point cloud of the external environment. In some examples, scanning LiDAR systems include a light source, a light transmitter, an optical steering mechanism, and a light detector. The light source generates a light beam that is directed by the optical steering mechanism in particular directions when being transmitted from the LiDAR system. When a transmitted light beam is scattered or reflected by an object, a portion of the scattered or reflected light returns to the LiDAR system to form a return light pulse. The light detector detects the return light pulse. Using the difference between the time that the return light pulse is detected and the time that a corresponding light pulse in the light beam is transmitted, the LiDAR system determines the distance to the object based on the speed of light. This data relating to the image or the 3D point cloud of the external environment (also referred to as the point cloud data or the LiDAR system output data) can be delivered to a vehicle perception and planning system and various other external systems, including intelligent infrastructure systems, and other vehicles for further processing and vehicular control.
Current and past versions of the data format of the LiDAR system output data associated with the image or point cloud of the external environment require a large number of bits; and thereby, these data formatting methods cause the LiDAR system to have low processing and communication efficiency. The LiDAR system disclosed herein, having data encoding and compression, reduces the number of bits, or uses the least number of bits, in the output data to carry the same or similar amount of information sufficient to define the image or point cloud of the external environment. Advantageously, the computational load is also reduced. This is especially helpful when the computational resources are limited in the LiDAR system or a vehicle onboard system to which the LiDAR system is connected. Less computational requirements also reduce communication bandwidth requirements and power consumption, which can be important for battery-powered vehicles.
Embodiments of present invention are described below. In various embodiments of the present invention, the method of data encoding and compression within a LiDAR system is provided. In particular, the method includes steering one or more beams of light using a plurality of reflective facets of the polygon mirror to scan a Field-Of-View (FOV) in a first direction. The method includes steering the one or more beams of light using a galvanometer mirror to scan the FOV in a second direction. After a return light pulse is reflected by an object in the FOV, the method includes receiving detection data from an optical detector of the scanning system, wherein the detection data is associated with the return light pulse. The method includes decoding the detection data into raw data and configuring output data from the raw data, trigger data, encoder data, and time synchronization data. The output data having a compressed format include a data set for one baseline channel and differential channel data for one or more channels based upon the baseline channel. In some embodiments, the point cloud output data can be sent in blocks, wherein each block corresponds to one laser firing and contains received data points from each one of the one or more channels. In some examples, the output data starts with a block header followed by data points of the point cloud. Representation of point cloud output data may comprise the use of a spherical coordinate system. Further, the method of data encoding and compression within a LiDAR system includes transmitting the trigger data to one or more sources of light to control emission timing of the one or more beams of light. The method also includes receiving the encoder data from the optical steering mechanism based on position encoders of the galvanometer mirror and the polygon mirror. In some embodiments, the output data and the time synchronization data are transmitted to external sources for further data processing.
In some embodiments, the LiDAR system having data encoding and compression is provided. In particular, the LiDAR system includes a laser source that supplies a plurality of light pulses to a optical steering mechanism, wherein the optical steering mechanism directs the plurality of light pulses in accordance with the FOV. In some examples, the optical steering mechanism comprises a mirror motor galvanometer for vertical motion scanning and a rotatable polygon mirror for horizontal motion scanning. The LiDAR system further includes an encoder/decoder processing agent coupled to receive detection data to transform the detection data into raw data and configure output data from the raw data, trigger data, encoder data, and time synchronization data. In some embodiments, the output data is in a compressed format including a data set for one baseline channel and differential channel data for one or more channels based upon the baseline channel. Accordingly, the point cloud output data can be sent in blocks, wherein each block corresponds to one laser firing and contains received data points from each one of the one or more channels. In some examples, the output data starts with a block header followed by data points of the point cloud. Representation of point cloud output data may comprise the use of a spherical coordinate system. The system may further include a controller coupled to receive data from the encoder/decoder processing agent. The controller couples to the optical steering mechanism and the laser source to coordinate movement speed of the integrated mirror motor galvanometer and the rotatable polygon mirror with the plurality of light pulses supplied by the laser source.
In typical configurations, motor vehicle 100 comprises one or more LiDAR systems 110 and 120A-120I. Each of LiDAR systems 110 and 120A-120I can be a scanning-based LiDAR system and/or a non-scanning LiDAR system (e.g., a flash LiDAR). A scanning-based LiDAR system scans one or more light beams in one or more directions (e.g., horizontal and vertical directions) to detect objects in a field-of-view (FOV). A non-scanning based LiDAR system transmits laser light to illuminate an FOV without scanning. For example, a flash LiDAR is a type of non-scanning based LiDAR system. A flash LiDAR can transmit laser light to simultaneously illuminate an FOV using a single light pulse or light shot.
A LiDAR system is a frequently-used sensor of a vehicle that is at least partially automated. In one embodiment, as shown in
In some embodiments, LiDAR systems 110 and 120A-120I are independent LiDAR systems having their own respective laser sources, control electronics, transmitters, receivers, and/or steering mechanisms. In other embodiments, some of LiDAR systems 110 and 120A-120I can share one or more components, thereby forming a distributed sensor system. In one example, optical fibers are used to deliver laser light from a centralized laser source to all LiDAR systems. For instance, system 110 (or another system that is centrally positioned or positioned anywhere inside the vehicle 100) includes a light source, a transmitter, and a light detector, but have no steering mechanisms. System 110 may distribute transmission light to each of systems 120A-120I. The transmission light may be distributed via optical fibers. Optical connectors can be used to couple the optical fibers to each of system 110 and 120A-120I. In some examples, one or more of systems 120A-120I include steering mechanisms but no light sources, transmitters, or light detectors. An optical steering mechanism may include one or more moveable mirrors such as one or more polygon mirrors, one or more single plane mirrors, one or more multi-plane mirrors, or the like. Embodiments of the light source, transmitter, optical steering mechanism, and light detector are described in more detail below. Through the steering mechanisms, one or more of systems 120A-120I scan light into one or more respective FOVs and receive corresponding return light. The return light is formed by scattering or reflecting the transmission light by one or more objects in the FOVs. Systems 120A-120I may also include collection lens and/or other optics to focus and/or direct the return light into optical fibers, which deliver the received return light to system 110. System 110 includes one or more light detectors for detecting the received return light. In some examples, system 110 is disposed inside a vehicle such that it is in a temperature-controlled environment, while one or more systems 120A-120I may be at least partially exposed to the external environment.
LiDAR system(s) 210 can include one or more of short-range LiDAR sensors, medium-range LiDAR sensors, and long-range LiDAR sensors. A short-range LiDAR sensor measures objects located up to about 20-50 meters from the LiDAR sensor. Short-range LiDAR sensors can be used for, e.g., monitoring nearby moving objects (e.g., pedestrians crossing street in a school zone), parking assistance applications, or the like. A medium-range LiDAR sensor measures objects located up to about 70-200 meters from the LiDAR sensor. Medium-range LiDAR sensors can be used for, e.g., monitoring road intersections, assistance for merging onto or leaving a freeway, or the like. A long-range LiDAR sensor measures objects located up to about 200 meters and beyond. Long-range LiDAR sensors are typically used when a vehicle is travelling at a high speed (e.g., on a freeway), such that the vehicle's control systems may only have a few seconds (e.g., 6-8 seconds) to respond to any situations detected by the LiDAR sensor. As shown in
With reference still to
Other vehicle onboard sensos(s) 230 can also include radar sensor(s) 234. Radar sensor(s) 234 use radio waves to determine the range, angle, and velocity of objects. Radar sensor(s) 234 produce electromagnetic waves in the radio or microwave spectrum. The electromagnetic waves reflect off an object and some of the reflected waves return to the radar sensor, thereby providing information about the object's position and velocity. Radar sensor(s) 234 can include one or more of short-range radar(s), medium-range radar(s), and long-range radar(s). A short-range radar measures objects located at about 0.1-30 meters from the radar. A short-range radar is useful in detecting objects located nearby the vehicle, such as other vehicles, buildings, walls, pedestrians, bicyclists, etc. A short-range radar can be used to detect a blind spot, assist in lane changing, provide rear-end collision warning, assist in parking, provide emergency braking, or the like. A medium-range radar measures objects located at about 30-80 meters from the radar. A long-range radar measures objects located at about 80-200 meters. Medium- and/or long-range radars can be useful in, for example, traffic following, adaptive cruise control, and/or highway automatic braking. Sensor data generated by radar sensor(s) 234 can also be provided to vehicle perception and planning system 220 via communication path 233 for further processing and controlling the vehicle operations. Radar sensor(s) 234 can be mount on, or integrated to, a vehicle at any locations (e.g., rear-view mirrors, pillars, front grille, and/or back bumpers, etc.).
Other vehicle onboard sensor(s) 230 can also include ultrasonic sensor(s) 236. Ultrasonic sensor(s) 236 use acoustic waves or pulses to measure object located external to a vehicle. The acoustic waves generated by ultrasonic sensor(s) 236 are transmitted to the surrounding environment. At least some of the transmitted waves are reflected off an object and return to the ultrasonic sensor(s) 236. Based on the return signals, a distance of the object can be calculated. Ultrasonic sensor(s) 236 can be useful in, for example, checking blind spots, identifying parking spaces, providing lane changing assistance into traffic, or the like. Sensor data generated by ultrasonic sensor(s) 236 can also be provided to vehicle perception and planning system 220 via communication path 233 for further processing and controlling the vehicle operations. Ultrasonic sensor(s) 236 can be mount on, or integrated to, a vehicle at any locations (e.g., rear-view mirrors, pillars, front grille, and/or back bumpers, etc.).
In some embodiments, one or more other sensor(s) 238 may be attached in a vehicle and may also generate sensor data. Other sensor(s) 238 may include, for example, global positioning systems (GPS), inertial measurement units (IMU), or the like. Sensor data generated by other sensor(s) 238 can also be provided to vehicle perception and planning system 220 via communication path 233 for further processing and controlling the vehicle operations. It is understood that communication path 233 may include one or more communication links to transfer data between the various sensor(s) 230 and vehicle perception and planning system 220.
In some embodiments, as shown in
With reference still to
Sharing sensor data facilitates a better perception of the environment external to the vehicles. For instance, a first vehicle may not sense a pedestrian that is behind a second vehicle but is approaching the first vehicle. The second vehicle may share the sensor data related to this pedestrian with the first vehicle such that the first vehicle can have additional reaction time to avoid collision with the pedestrian. In some embodiments, similar to data generated by sensor(s) 230, data generated by sensors onboard other vehicle(s) 250 may be correlated or fused with sensor data generated by LiDAR system(s) 210 (or with other LiDAR systems located in other vehicles), thereby at least partially offloading the sensor fusion process performed by vehicle perception and planning system 220.
In some embodiments, intelligent infrastructure system(s) 240 are used to provide sensor data separately or together with LiDAR system(s) 210. Certain infrastructures may be configured to communicate with a vehicle to convey information and vice versa. Communications between a vehicle and infrastructures are generally referred to as V2I (vehicle to infrastructure) communications. For example, intelligent infrastructure system(s) 240 may include an intelligent traffic light that can convey its status to an approaching vehicle in a message such as “changing to yellow in 5 seconds.” Intelligent infrastructure system(s) 240 may also include its own LiDAR system mounted near an intersection such that it can convey traffic monitoring information to a vehicle. For example, a left-turning vehicle at an intersection may not have sufficient sensing capabilities because some of its own sensors may be blocked by traffic in the opposite direction. In such a situation, sensors of intelligent infrastructure system(s) 240 can provide useful data to the left-turning vehicle. Such data may include, for example, traffic conditions, information of objects in the direction the vehicle is turning to, traffic light status and predictions, or the like. These sensor data generated by intelligent infrastructure system(s) 240 can be provided to vehicle perception and planning system 220 and/or vehicle onboard LiDAR system(s) 210, via communication paths 243 and/or 241, respectively. Communication paths 243 and/or 241 can include any wired or wireless communication links that can transfer data. For example, sensor data from intelligent infrastructure system(s) 240 may be transmitted to LiDAR system(s) 210 and correlated or fused with sensor data generated by LiDAR system(s) 210, thereby at least partially offloading the sensor fusion process performed by vehicle perception and planning system 220. V2V and V2I communications described above are examples of vehicle-to-X (V2X) communications, where the “X” represents any other devices, systems, sensors, infrastructure, or the like that can share data with a vehicle.
With reference still to
In other examples, sensor data generated by other vehicle onboard sensor(s) 230 may have a lower resolution (e.g., radar sensor data) and thus may need to be correlated and confirmed by LiDAR system(s) 210, which usually has a higher resolution. For example, a sewage cover (also referred to as a manhole cover) may be detected by radar sensor 234 as an object towards which a vehicle is approaching. Due to the low-resolution nature of radar sensor 234, vehicle perception and planning system 220 may not be able to determine whether the object is an obstacle that the vehicle needs to avoid. High-resolution sensor data generated by LiDAR system(s) 210 thus can be used to correlated and confirm that the object is a sewage cover and causes no harm to the vehicle.
Vehicle perception and planning system 220 further comprises an object classifier 223. Using raw sensor data and/or correlated/fused data provided by sensor fusion sub-system 222, object classifier 223 can use any computer vision techniques to detect and classify the objects and estimate the positions of the objects. In some embodiments, object classifier 223 can use machine-learning based techniques to detect and classify objects. Examples of the machine-learning based techniques include utilizing algorithms such as region-based convolutional neural networks (R-CNN), Fast R-CNN, Faster R-CNN, histogram of oriented gradients (HOG), region-based fully convolutional network (R-FCN), single shot detector (SSD), spatial pyramid pooling (SPP-net), and/or You Only Look Once (Yolo).
Vehicle perception and planning system 220 further comprises a road detection sub-system 224. Road detection sub-system 224 localizes the road and identifies objects and/or markings on the road. For example, based on raw or fused sensor data provided by radar sensor(s) 234, camera(s) 232, and/or LiDAR system(s) 210, road detection sub-system 224 can build a 3D model of the road based on machine-learning techniques (e.g., pattern recognition algorithms for identifying lanes). Using the 3D model of the road, road detection sub-system 224 can identify objects (e.g., obstacles or debris on the road) and/or markings on the road (e.g., lane lines, turning marks, crosswalk marks, or the like).
Vehicle perception and planning system 220 further comprises a localization and vehicle posture sub-system 225. Based on raw or fused sensor data, localization and vehicle posture sub-system 225 can determine position of the vehicle and the vehicle's posture. For example, using sensor data from LiDAR system(s) 210, camera(s) 232, and/or GPS data, localization and vehicle posture sub-system 225 can determine an accurate position of the vehicle on the road and the vehicle's six degrees of freedom (e.g., whether the vehicle is moving forward or backward, up or down, and left or right). In some embodiments, high-definition (HD) maps are used for vehicle localization. HD maps can provide highly detailed, three-dimensional, computerized maps that pinpoint a vehicle's location. For instance, using the HD maps, localization and vehicle posture sub-system 225 can determine precisely the vehicle's current position (e.g., which lane of the road the vehicle is currently in, how close it is to a curb or a sidewalk) and predict vehicle's future positions.
Vehicle perception and planning system 220 further comprises obstacle predictor 226. Objects identified by object classifier 223 can be stationary (e.g., a light pole, a road sign) or dynamic (e.g., a moving pedestrian, bicycle, another car). For moving objects, predicting their moving path or future positions can be important to avoid collision. Obstacle predictor 226 can predict an obstacle trajectory and/or warn the driver or the vehicle planning sub-system 228 about a potential collision. For example, if there is a high likelihood that the obstacle's trajectory intersects with the vehicle's current moving path, obstacle predictor 226 can generate such a warning. Obstacle predictor 226 can use a variety of techniques for making such a prediction. Such techniques include, for example, constant velocity or acceleration models, constant turn rate and velocity/acceleration models, Kalman Filter and Extended Kalman Filter based models, recurrent neural network (RNN) based models, long short-term memory (LSTM) neural network based models, encoder-decoder RNN models, or the like.
With reference still to
Vehicle control system 280 controls the vehicle's steering mechanism, throttle, brake, etc., to operate the vehicle according to the planned route and movement. In some examples, vehicle perception and planning system 220 may further comprise a user interface 260, which provides a user (e.g., a driver) access to vehicle control system 280 to, for example, override or take over control of the vehicle when necessary. User interface 260 may also be separate from vehicle perception and planning system 220. User interface 260 can communicate with vehicle perception and planning system 220, for example, to obtain and display raw or fused sensor data, identified objects, vehicle's location/posture, etc. These displayed data can help a user to better operate the vehicle. User interface 260 can communicate with vehicle perception and planning system 220 and/or vehicle control system 280 via communication paths 221 and 261 respectively, which include any wired or wireless communication links that can transfer data. It is understood that the various systems, sensors, communication links, and interfaces in
In some embodiments, LiDAR system 300 can be a coherent LiDAR system. One example is a frequency-modulated continuous-wave (FMCW) LiDAR. Coherent LiDARs detect objects by mixing return light from the objects with light from the coherent laser transmitter. Thus, as shown in
LiDAR system 300 can also include other components not depicted in
Light source 310 outputs laser light for illuminating objects in a field of view (FOV). The laser light can be infrared light having a wavelength in the range of 700 nm to 1 mm. Light source 310 can be, for example, a semiconductor-based laser (e.g., a diode laser) and/or a fiber-based laser. A semiconductor-based laser can be, for example, an edge emitting laser (EEL), a vertical cavity surface emitting laser (VCSEL), an external-cavity diode laser, a vertical-external-cavity surface-emitting laser, a distributed feedback (DFB) laser, a distributed Bragg reflector (DBR) laser, an interband cascade laser, a quantum cascade laser, a quantum well laser, a double heterostructure laser, or the like. A fiber-based laser is a laser in which the active gain medium is an optical fiber doped with rare-earth elements such as erbium, ytterbium, neodymium, dysprosium, praseodymium, thulium and/or holmium. In some embodiments, a fiber laser is based on double-clad fibers, in which the gain medium forms the core of the fiber surrounded by two layers of cladding. The double-clad fiber allows the core to be pumped with a high-power beam, thereby enabling the laser source to be a high power fiber laser source.
In some embodiments, light source 310 comprises a master oscillator (also referred to as a seed laser) and power amplifier (MOPA). The power amplifier amplifies the output power of the seed laser. The power amplifier can be a fiber amplifier, a bulk amplifier, or a semiconductor optical amplifier. The seed laser can be a diode laser (e.g., a Fabry-Perot cavity laser, a distributed feedback laser), a solid-state bulk laser, or a tunable external-cavity diode laser. In some embodiments, light source 310 can be an optically pumped microchip laser. Microchip lasers are alignment-free monolithic solid-state lasers where the laser crystal is directly contacted with the end mirrors of the laser resonator. A microchip laser is typically pumped with a laser diode (directly or using a fiber) to obtain the desired output power. A microchip laser can be based on neodymium-doped yttrium aluminum garnet (Y3Al5O12) laser crystals (i.e., Nd:YAG), or neodymium-doped vanadate (i.e., ND:YVO4) laser crystals. In some examples, light source 310 may have multiple amplification stages to achieve a high power gain such that the laser output can have high power, thereby enabling the LiDAR system to have a long scanning range. In some examples, the power amplifier of light source 310 can be controlled such that the power gain can be varied to achieve any desired laser output power.
Referencing to
It is understood that the above descriptions provide non-limiting examples of a light source 310. Light source 310 can be configured to include many other types of light sources (e.g., laser diodes, short-cavity fiber lasers, solid-state lasers, and/or tunable external cavity diode lasers) that are configured to generate one or more light signals at various wavelengths. In some examples, light source 310 comprises amplifiers (e.g., pre-amplifiers and/or booster amplifiers), which can be a doped optical fiber amplifier, a solid-state bulk amplifier, and/or a semiconductor optical amplifier. The amplifiers are configured to receive and amplify light signals with desired gains.
With reference to
Laser beams provided by light source 310 may diverge as they travel to transmitter 320. Therefore, transmitter 320 often comprises a collimating lens configured to collect the diverging laser beams and produce more parallel optical beams with reduced or minimum divergence. The collimated optical beams can then be further directed through various optics such as mirrors and lens. A collimating lens may be, for example, a single plano-convex lens or a lens group. The collimating lens can be configured to achieve any desired properties such as the beam diameter, divergence, numerical aperture, focal length, or the like. A beam propagation ratio or beam quality factor (also referred to as the M2 factor) is used for measurement of laser beam quality. In many LiDAR applications, it is important to have good laser beam quality in the generated transmitting laser beam. The M2 factor represents a degree of variation of a beam from an ideal Gaussian beam. Thus, the M2 factor reflects how well a collimated laser beam can be focused on a small spot, or how well a divergent laser beam can be collimated. Therefore, light source 310 and/or transmitter 320 can be configured to meet, for example, a scan resolution requirement while maintaining the desired M2 factor.
One or more of the light beams provided by transmitter 320 are scanned by optical steering mechanism 340 to a FOV. Optical steering mechanism 340 scans light beams in multiple dimensions (e.g., in both the horizontal and vertical dimension) to facilitate LiDAR system 300 to map the environment by generating a 3D point cloud. A horizontal dimension can be a dimension that is parallel to the horizon or a surface associated with the LiDAR system or a vehicle (e.g., a road surface). A vertical dimension is perpendicular to the horizontal dimension (i.e., the vertical dimension forms a 90-degree angle with the horizontal dimension). Optical steering mechanism 340 will be described in more detail below. The laser light scanned to an FOV may be scattered or reflected by an object in the FOV. At least a portion of the scattered or reflected light forms return light that returns to LiDAR system 300.
A light detector detects the return light focused by the optical receiver and generates current and/or voltage signals proportional to the incident intensity of the return light. Based on such current and/or voltage signals, the depth information of the object in the FOV can be derived. One example method for deriving such depth information is based on the direct TOF (time of flight), which is described in more detail below. A light detector may be characterized by its detection sensitivity, quantum efficiency, detector bandwidth, linearity, signal to noise ratio (SNR), overload resistance, interference immunity, etc. Based on the applications, the light detector can be configured or customized to have any desired characteristics. For example, optical receiver and light detector 330 can be configured such that the light detector has a large dynamic range while having a good linearity. The light detector linearity indicates the detector's capability of maintaining linear relationship between input optical signal power and the detector's output. A detector having good linearity can maintain a linear relationship over a large dynamic input optical signal range.
To achieve desired detector characteristics, configurations or customizations can be made to the light detector's structure and/or the detector's material system. Various detector structure can be used for a light detector. For example, a light detector structure can be a PIN based structure, which has a undoped intrinsic semiconductor region (i.e., an “i” region) between a p-type semiconductor and an n-type semiconductor region. Other light detector structures comprise, for example, an APD (avalanche photodiode) based structure, a PMT (photomultiplier tube) based structure, a SiPM (Silicon photomultiplier) based structure, a SPAD (single-photon avalanche diode) based structure, and/or quantum wires. For material systems used in a light detector, Si, InGaAs, and/or Si/Ge based materials can be used. It is understood that many other detector structures and/or material systems can be used in optical receiver and light detector 330.
A light detector (e.g., an APD based detector) may have an internal gain such that the input signal is amplified when generating an output signal. However, noise may also be amplified due to the light detector's internal gain. Common types of noise include signal shot noise, dark current shot noise, thermal noise, and amplifier noise. In some embodiments, optical receiver and light detector 330 may include a pre-amplifier that is a low noise amplifier (LNA). In some embodiments, the pre-amplifier may also include a transimpedance amplifier (TIA), which converts a current signal to a voltage signal. For a linear detector system, input equivalent noise or noise equivalent power (NEP) measures how sensitive the light detector is to weak signals. Therefore, they can be used as indicators of the overall system performance. For example, the NEP of a light detector specifies the power of the weakest signal that can be detected and therefore it in turn specifies the maximum range of a LiDAR system. It is understood that various light detector optimization techniques can be used to meet the requirement of LiDAR system 300. Such optimization techniques may include selecting different detector structures, materials, and/or implementing signal processing techniques (e.g., filtering, noise reduction, amplification, or the like). For example, in addition to, or instead of, using direct detection of return signals (e.g., by using ToF), coherent detection can also be used for a light detector. Coherent detection allows for detecting amplitude and phase information of the received light by interfering the received light with a local oscillator. Coherent detection can improve detection sensitivity and noise immunity.
Optical steering mechanism 340 can be used with a transceiver (e.g., transmitter 320 and optical receiver and light detector 330) to scan the FOV for generating an image or a 3D point cloud. As an example, to implement optical steering mechanism 340, a two-dimensional mechanical scanner can be used with a single-point or several single-point transceivers. A single-point transceiver transmits a single light beam or a small number of light beams (e.g., 2-8 beams) to the optical steering mechanism. A two-dimensional mechanical optical steering mechanism comprises, for example, polygon mirror(s), oscillating mirror(s), rotating prism(s), rotating tilt mirror surface(s), single-plane or multi-plane mirror(s), or a combination thereof. In some embodiments, optical steering mechanism 340 may include non-mechanical optical steering mechanism(s) such as solid-state optical steering mechanism(s). For example, optical steering mechanism 340 can be based on tuning wavelength of the laser light combined with refraction effect, and/or based on reconfigurable grating/phase array. In some embodiments, optical steering mechanism 340 can use a single scanning device to achieve two-dimensional scanning or multiple scanning devices combined to realize two-dimensional scanning.
As another example, to implement optical steering mechanism 340, a one-dimensional mechanical scanner can be used with an array or a large number of single-point transceivers. Specifically, the transceiver array can be mounted on a rotating platform to achieve 360-degree horizontal field of view. Alternatively, a static transceiver array can be combined with the one-dimensional mechanical scanner. A one-dimensional mechanical scanner comprises polygon mirror(s), oscillating mirror(s), rotating prism(s), rotating tilt mirror surface(s), or a combination thereof, for obtaining a forward-looking horizontal field of view. Optical steering mechanisms using mechanical scanners can provide robustness and reliability in high volume production for automotive applications.
As another example, to implement optical steering mechanism 340, a two-dimensional transceiver can be used to generate a scan image or a 3D point cloud directly. In some embodiments, a stitching or micro shift method can be used to improve the resolution of the scan image or the field of view being scanned. For example, using a two-dimensional transceiver, signals generated at one direction (e.g., the horizontal direction) and signals generated at the other direction (e.g., the vertical direction) may be integrated, interleaved, and/or matched to generate a higher or full resolution image or 3D point cloud representing the scanned FOV.
Some implementations of optical steering mechanism 340 comprise one or more optical redirection elements (e.g., mirrors or lenses) that steer return light signals (e.g., by rotating, vibrating, or directing) along a receive path to direct the return light signals to optical receiver and light detector 330. The optical redirection elements that direct light signals along the transmitting and receiving paths may be the same components (e.g., shared), separate components (e.g., dedicated), and/or a combination of shared and separate components. This means that in some cases the transmitting and receiving paths are different although they may partially overlap (or in some cases, substantially overlap or completely overlap).
With reference still to
Control circuitry 350 can also be configured and/or programmed to perform signal processing to the raw data generated by optical receiver and light detector 330 to derive distance and reflectance information, and perform data configuration such as encoding and compression. Control circuitry 350 can communicate configured output data to vehicle perception and planning system 220 (shown in
LiDAR system 300 can be disposed in a vehicle, which may operate in many different environments including hot or cold weather, rough road conditions that may cause intense vibration, high or low humidities, dusty areas, etc. Therefore, in some embodiments, optical and/or electronic components of LiDAR system 300 (e.g., optics in transmitter 320, optical receiver and light detector 330, and optical steering mechanism 340) are disposed and/or configured in such a manner to maintain long term mechanical and optical stability. For example, components in LiDAR system 300 may be secured and sealed such that they can operate under all conditions a vehicle may encounter. As an example, an anti-moisture coating and/or hermetic sealing may be applied to optical components of transmitter 320, optical receiver and light detector 330, and optical steering mechanism 340 (and other components that are susceptible to moisture). As another example, housing(s), enclosure(s), fairing(s), and/or window can be used in LiDAR system 300 for providing desired characteristics such as hardness, ingress protection (IP) rating, self-cleaning capability, resistance to chemical and resistance to impact, or the like. In addition, efficient and economical methodologies for assembling LiDAR system 300 may be used to meet the LiDAR operating requirements while keeping the cost low.
It is understood by a person of ordinary skill in the art that
As described above, LiDAR system 300 includes a control circuitry 350. With reference to
In some embodiments, encoder/decoder processing agent 380 couples to receive detection data from optical receiver and light detector 330 via communication path 362. Encoder/decoder processing agent 380 transforms the detection data into raw data. The detection data may include, for example analog or digital data (provided by an analog-to-digital converter) representing the return light pulses. Agent 380 further configures output data from trigger data, encoder data, time synchronization data, and the raw data. Each of these types of data are described in greater details below. Encoder/decoder processing agent 380 configures the output data in a compressed format that includes a data set for one baseline channel and differential channel data for one or more channels based upon the baseline channel. For instance, LiDAR system 300 may have 4 transceiver channels, each of which are used to obtain detection data, from which raw data are generated. The first transceiver channel may be used as a baseline channel, and the data of the additional 3 channels are referenced to the baseline channel, thereby implementing the compression of data for the 3 additional channels. This compressed format of the output data conserves storage space and processing time. Furthermore, the compressed data format can reduce the bandwidth requirements for providing the output data to downstream processing (e.g., perception or sensor fusion based on the LiDAR system's point cloud output data).
In some examples, light source 310 couples to receive the trigger data from control circuitry 350 to control emission timing of the one or more of light pulses provided by light source 310. The trigger data may be sent from a controller 370 to light source 310. This trigger data can be used to calculate object distances and thus are often included as a part of the point cloud output data of the LiDAR system for enabling downstream processing. The trigger data can be obtained from the controller 370 and included in the compressed output data provided by the LiDAR system for downstream processing. Optical steering mechanism 340 may include position encoders for galvanometer 347 and polygon mirror 345. These position encoders provide position encoder data such as angular positions of mirror motor galvanometer 347 and rotatable polygon mirror 345, timestamps, region of interests (ROI), etc. In some examples, the encoder data are provided by the optical steering mechanism 340 to encoder/decoder processing agent 380 of control circuitry 350. Controller 370 couples to receive the encoder data from the encoder/decoder processing agent 380. Based on the encoder data, controller 370 is coupled to the optical steering mechanism 340 and the light source 310 to coordinate movement speed of the integrated mirror motor galvanometer 347 and the rotatable polygon mirror 345 with the plurality of light pulses supplied by the laser source 310.
Receiver 382 of encoder/decoder processing agent 380 couples to receive detection data representing optical signals 362. The optical signals 362 are detected return light pulses formed by objects in the FOV. Data pre-processor 386 performs filtering in preparation to transform the detection data into raw data. For instance, unwanted signals such as noise, scattered light signals from internal of the LiDAR system are filtered out. The filtering can be performed in analog and/or digital domain. Detection data include pulse data such as pulse intensities and pulse time stamps. In some embodiments, data pre-processor unit 386 also performs some of the initial encoding of analog data from receiver 382 and the time stamps into raw encoded data. Decoder 388 couples to receive the raw encoded data and decode the same. For example, decoder 388 may perform some associated calculations (e.g., from time stamps to ToF and angles). Encoder compression engine 389 couples to receive the decoded data to transform the data into raw data. In some examples, encoder compression engine 389 also receives trigger data, encoder data, and time synchronization data. By using these data and raw data, encoder compression engine 389 configures output data. In a multiple transceiver channels setting, encoder compression engine 389 configures the output data in a compressed format that includes a data set for one baseline channel and differential channel data for one or more channels based upon the baseline channel. This compressed format conserves storage space and processing time. The output data in compressed format can thus be efficiently stored or communicated to external systems for down streaming processing.
With continued reference to
Trigger data represents the time value of when receiver 382 detects a part of the light pulse transmitted through a feedback channel. That is, a part of the light pulse is reflected back internally (inside the LiDAR system's optical pathway) toward the receiver 382. When the receiver 382 (through the feedback channel) detects that part of the light pulse, the trigger data is generated with a timestamp of when receiver 382 detects that the light pulse has been transmitted. Since the timing pattern of the light pulses may not be constant, there may also be delays in the LiDAR system. As a result, the actual time of when the light pulse is transmitted out of the LiDAR system is detected, as opposed to when the controller sent the trigger signal to the laser. This physical feedback detection of the light pulse, as the trigger data, represents Time 0 for the Time of flight (ToF) calculation.
In some embodiments, processor 357 is a field programmable gate array (FPGA) or an application specific integrated circuit (ASIC) type processor. Encoder/decoder processing agent 380 can include one or more processors and/or be combined with processor 357. Processor 357 in cooperation with the encoder/decoder processing agent 380 sends output data having four data types to an internal or external system having software models or algorithms for subsequent processing (e.g., perception, sensor fusion, etc.). The output data having these four data types can be sent in a single data stream in the order in which events corresponding to the data happen. Each data type uses 3 or 4 bits at the beginning to identify which data type is sent. The downstream systems can thus decode the received point cloud data based on the data type accordingly. The data “type” indicates which one or more of the four data sets are encoded in the data stream. The four data sets include, for example, trigger data, encoder data, raw data, and time synchronization data.
With continued reference to
Referring back to
With reference back to
In some examples, the reference pulse flag in raw data 406 indicates that a detected pulse is a reference pulse, wherein the pulse is sent directly from a transmitter 320 to the receiver 330 (shown in
With continued reference to
In some examples, time synchronization data 408 includes 16 bytes (i.e., 128 bits), where four (4) bits of a data type identification segment represent the data type and four (4) bits of a sub-type segment represent a sub-type. The data type bits and sub-type bits are number values used to indicate each type and each sub-type. In some examples, the time synchronization data 408 may include a data type of 0 represented in binary bits (i.e., “0000”) indicating that this data set is the type of time synchronization data. The different sub-types may use different sub-type bits. If sub-type is represented by 0 in binary bits (i.e., “0000”), it indicates the data set is represented using precision time protocol (ptp). The time synchronization data 408 may include a sub-type of 1 represented in binary bits (i.e., “0001”) for network time protocol (ntp) or a sub-type of 15 (“1111”) for Global Positioning System (GPS) time stamp. GPS reference time is referenced to UTC with zero point defined as midnight on the night of Jan. 5, 1980. The time stamp comprises the number of weeks since that zero point and the number of seconds since the last week number change (0 to 604,799). Time synchronization data 408 also includes data used for hardware time and synchronized time. These data are represented using 78 bits in a synchronization and hardware time data segment. The time synchronization data 408 is used both internally by the encoder compression engine 389 and sent externally. Internally, it can be used to generate time stamps in the compressed data stream. Externally, it can be used by a software model or algorithm of an external system to view and analyze the raw encoded data, which like the compressed data stream, can be recorded in point cloud data files.
The LiDAR system, via the control circuitry, sends the output data in a data stream comprising blocks of data. The output data are also referred to as the point cloud data. In some examples, each block corresponds to one laser firing and contains received data points from all four channels. A block starts with a block header followed by information for the data points. The representation of point cloud uses a spherical coordinate system. As shown by the output data block header format 426 in
In a four-channel configuration, the other three channel angles are encoded as the differences with respect to channel 0's angles. The actual horizontal angle is the difference plus channel 0's horizontal angle. Vertical angles have a non-zero offset so that the actual vertical angle is the difference plus channel 0's vertical angle and the non-zero offset. The offset values used for channel 1/2/3 may be different. The differences for the other three channels' angle data are stored in the corresponding channel horizontal and vertical angle data segments in the block header. The underlying reason for encoding angles in this way is to use the least number of bits to represent angles. For example, with this differential encoding method, channel 1 uses 9 bits for horizontal angle difference and 8 bits for vertical angle difference. Channel 2 uses 10 bits for horizontal angle difference and 9 bits for vertical angle difference. Channel 3 uses 11 bits for horizontal angle difference and 9 bits for vertical angle difference. Thus, encoding the angles in this manner effectively compresses the number of bits needed to encode angles of 4 channels significantly, because 3 of the 4 channel's angles are encoded as difference values from channel 0. This format for the output data is efficient without sacrificing angle resolution. It is understood that more channels may be included in a LiDAR system (e.g., 6, 8, 12, . . . 64, etc.). The differential encoding method can be used for any number of channels, and the compression can be even more significant when the number of channels increases.
Further, the block header of output data shown in
Comparing
Following the 17 bytes block header as illustrated by the output data block header format 426 in
In some embodiments, the number of data points in a block can be a variable. For example, the block may comprise four data points, when each channel outputs at most 1 point in each laser firing. In the alternative, the block may comprise eight points, when each channel outputs at most 2 points during each laser firing. In an example implementation, the data point format 428 includes 17 bits for radius; 9 bits for intensity/reflectance; 2 bits for point type; and 4 bits for pulse elongation/width.
Overall, by eliminating unnecessary data and/or compressing data (e.g., using differential encoding), the embodiments of the present disclosure significantly reduce the number of bits required for the output data. In turn, this reduces the amount of data to be processed downstream and to be communicated to other systems. As a result, the embodiments of the present disclosure reduce the communication bandwidth requirements, reduce the processing power, and improve the computing efficiency.
In some examples, the output data starts with a block header followed by information for the data points of the point cloud. Representation of point cloud output data may comprise the use of a spherical coordinate system. Although not shown, method 500 of data encoding and compression within a LiDAR system may include transmitting the trigger data to one or more sources of light to control emission timing of the one or more beams of light. Method 500 may also include receiving the encoder data from the optical steering mechanism, wherein the encoder data are generated by position encoders of the galvanometer mirror and the polygon mirror. In some embodiments, the output data and the time synchronization data can be transmitted to external sources for further data processing. Method 500 may also include other actions described in this disclosure.
In some variations, fiber-based laser source 600 can be controlled (e.g., by control circuitry 350) to produce pulses of different amplitudes based on the fiber gain profile of the fiber used in fiber-based laser source 600. Communication path 312 couples fiber-based laser source 600 to control circuitry 350 (shown in
As described above, some LiDAR systems use the time-of-flight (ToF) of light signals (e.g., light pulses) to determine the distance to objects in a light path. For example, with reference to
Referring back to
By directing many light pulses, as depicted in
If a corresponding light pulse is not received for a particular transmitted light pulse, then LiDAR system 700 may determine that there are no objects within a detectable range of LiDAR system 700 (e.g., an object is beyond the maximum scanning distance of LiDAR system 700). For example, in
In
The density of a point cloud refers to the number of measurements (data points) per area performed by the LiDAR system. A point cloud density relates to the LiDAR scanning resolution. Typically, a larger point cloud density, and therefore a higher resolution, is desired at least for the region of interest (ROI). The density of points in a point cloud or image generated by a LiDAR system is equal to the number of pulses divided by the field of view. In some embodiments, the field of view can be fixed. Therefore, to increase the density of points generated by one set of transmission-receiving optics (or transceiver optics), the LiDAR system may need to generate a pulse more frequently. In other words, a light source in the LiDAR system may have a higher pulse repetition rate (PRR). On the other hand, by generating and transmitting pulses more frequently, the farthest distance that the LiDAR system can detect may be limited. For example, if a return signal from a distant object is received after the system transmits the next pulse, the return signals may be detected in a different order than the order in which the corresponding signals are transmitted, thereby causing ambiguity if the system cannot correctly correlate the return signals with the transmitted signals.
To illustrate, consider an example LiDAR system that can transmit laser pulses with a pulse repetition rate between 500 kHz and 1 MHz. Based on the time it takes for a pulse to return to the LiDAR system and to avoid mix-up of return pulses from consecutive pulses in a typical LiDAR design, the farthest distance the LiDAR system can detect may be 300 meters and 150 meters for 500 kHz and 1 MHz, respectively. The density of points of a LiDAR system with 500 kHz repetition rate is half of that with 1 MHz. Thus, this example demonstrates that, if the system cannot correctly correlate return signals that arrive out of order, increasing the repetition rate from 500 kHz to 1 MHz (and thus improving the density of points of the system) may reduce the detection range of the system. Various techniques are used to mitigate the tradeoff between higher PRR and limited detection range. For example, multiple wavelengths can be used for detecting objects in different ranges. Optical and/or signal processing techniques (e.g., pulse encoding techniques) are also used to correlate between transmitted and return light signals.
Various systems, apparatus, and methods described herein may be implemented using digital circuitry, or using one or more computers using well-known computer processors, memory units, storage devices, computer software, and other components. Typically, a computer includes a processor for executing instructions and one or more memories for storing instructions and data. A computer may also include, or be coupled to, one or more mass storage devices, such as one or more magnetic disks, internal hard disks and removable disks, magneto-optical disks, optical disks, etc.
Various systems, apparatus, and methods described herein may be implemented using computers operating in a client-server relationship. Typically, in such a system, the client computers are located remotely from the server computers and interact via a network. The client-server relationship may be defined and controlled by computer programs running on the respective client and server computers. Examples of client computers can include desktop computers, workstations, portable computers, cellular smartphones, tablets, or other types of computing devices.
Various systems, apparatus, and methods described herein may be implemented using a computer program product tangibly embodied in an information carrier, e.g., in a non-transitory machine-readable storage device, for execution by a programmable processor; and the method processes and steps described herein, including one or more of the steps of encoding and compression of point cloud data of
A high-level block diagram of an example apparatus that may be used to implement systems, apparatus and methods described herein is illustrated in
Processor 810 may include both general and special purpose microprocessors and may be the sole processor or one of multiple processors of apparatus 800. Processor 810 may comprise one or more central processing units (CPUs), and one or more graphics processing units (GPUs), which, for example, may work separately from and/or multi-task with one or more CPUs to accelerate processing, e.g., for various image processing applications described herein. Processor 810, persistent storage device 820, and/or main memory device 830 may include, be supplemented by, or incorporated in, one or more application-specific integrated circuits (ASICs) and/or one or more field programmable gate arrays (FPGAs).
Persistent storage device 820 and main memory device 830 each comprise a tangible non-transitory computer readable storage medium. Persistent storage device 820, and main memory device 830, may each include high-speed random access memory, such as dynamic random access memory (DRAM), static random access memory (SRAM), double data rate synchronous dynamic random access memory (DDR RAM), or other random access solid state memory devices, and may include non-volatile memory, such as one or more magnetic disk storage devices such as internal hard disks and removable disks, magneto-optical disk storage devices, optical disk storage devices, flash memory devices, semiconductor memory devices, such as erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM), digital versatile disc read-only memory (DVD-ROM) disks, or other non-volatile solid state storage devices.
Input/output devices 890 may include peripherals, such as a printer, scanner, display screen, etc. For example, input/output devices 890 may include a display device such as a cathode ray tube (CRT), plasma or liquid crystal display (LCD) monitor for displaying information to a user, a keyboard, and a pointing device such as a mouse or a trackball by which the user can provide input to apparatus 800.
Any or all of the functions of the systems and apparatuses discussed herein may be performed by processor 810, and/or incorporated in, an apparatus or a system such as LiDAR system 300. Further, LiDAR system 300 and/or apparatus 800 may utilize one or more neural networks or other deep-learning techniques performed by processor 810 or other systems or apparatuses discussed herein.
One skilled in the art will recognize that an implementation of an actual computer or computer system may have other structures and may contain other components as well, and that
The foregoing specification is to be understood as being in every respect illustrative and exemplary, but not restrictive, and the scope of the invention disclosed herein is not to be determined from the specification, but rather from the claims as interpreted according to the full breadth permitted by the patent laws. It is to be understood that the embodiments shown and described herein are only illustrative of the principles of the present invention and that various modifications may be implemented by those skilled in the art without departing from the scope and spirit of the invention. Those skilled in the art could implement various other feature combinations without departing from the scope and spirit of the invention.
This application claims priority to U.S. Provisional Patent Application Ser. No. 63/324,012, filed Mar. 25, 2022, entitled “LIDAR SYSTEM COMMUNICATION USING EFFECTIVE DATA FORMATTING OF THE POINT CLOUD,” the content of which is hereby incorporated by reference in its entirety for all purposes.
| Number | Date | Country | |
|---|---|---|---|
| 63324012 | Mar 2022 | US |
