Systems and methods for camera alignment using pre-distorted targets

Provided are systems and methods for camera alignment using pre-distorted targets. Some methods described include selecting a configuration of shapes, and determining targets by pre-distorting the shapes according to the inverse of the distortion function of the lens system to be aligned. Images of pre-distorted targets are then compared to the original configuration of shapes, to perform camera alignment. Alignment is thus accomplished in simpler and more accurate manner. Systems and computer program products are also provided.

BACKGROUND

Current digital camera systems often employ one or more lenses, and a sensor array to detect light or other radiation focused by the lenses. Alignment between lenses and the sensor array is an important factor in camera performance, including in applications such as autonomous vehicle systems. Camera alignment is not without its challenges, however. For example, alignment processes commonly employ flat target boards and collimator targets which, in systems such as wide angle cameras, may appear excessively distorted and thus lead to inaccurate alignment. Alignment tolerances in both the camera itself and its assembly may also lead to inaccurate estimation of lens center, resulting in difficulties when performing processes such as camera calibration.

DETAILED DESCRIPTION

In the following description numerous specific details are set forth in order to provide a thorough understanding of the present disclosure for the purposes of explanation. It will be apparent, however, that the embodiments described by the present disclosure can be practiced without these specific details. In some instances, well-known structures and devices are illustrated in block diagram form in order to avoid unnecessarily obscuring aspects of the present disclosure.

Specific arrangements or orderings of schematic elements, such as those representing systems, devices, modules, instruction blocks, data elements, and/or the like are illustrated in the drawings for ease of description. However, it will be understood by those skilled in the art that the specific ordering or arrangement of the schematic elements in the drawings is not meant to imply that a particular order or sequence of processing, or separation of processes, is required unless explicitly described as such. Further, the inclusion of a schematic element in a drawing is not meant to imply that such element is required in all embodiments or that the features represented by such element may not be included in or combined with other elements in some embodiments unless explicitly described as such.

Further, where connecting elements such as solid or dashed lines or arrows are used in the drawings to illustrate a connection, relationship, or association between or among two or more other schematic elements, the absence of any such connecting elements is not meant to imply that no connection, relationship, or association can exist. In other words, some connections, relationships, or associations between elements are not illustrated in the drawings so as not to obscure the disclosure. In addition, for ease of illustration, a single connecting element can be used to represent multiple connections, relationships, or associations between elements. For example, where a connecting element represents communication of signals, data, or instructions (e.g., “software instructions”), it should be understood by those skilled in the art that such element can represent one or multiple signal paths (e.g., a bus), as may be needed, to affect the communication.

Although the terms first, second, third, and/or the like are used to describe various elements, these elements should not be limited by these terms. The terms first, second, third, and/or the like are used only to distinguish one element from another. For example, a first contact could be termed a second contact and, similarly, a second contact could be termed a first contact without departing from the scope of the described embodiments. The first contact and the second contact are both contacts, but they are not the same contact.

As used herein, the term “if” is, optionally, construed to mean “when”, “upon”, “in response to determining,” “in response to detecting,” and/or the like, depending on the context. Similarly, the phrase “if it is determined” or “if [a stated condition or event] is detected” is, optionally, construed to mean “upon determining,” “in response to determining,” “upon detecting [the stated condition or event],” “in response to detecting [the stated condition or event],” and/or the like, depending on the context. Also, as used herein, the terms “has”, “have”, “having”, or the like are intended to be open-ended terms. Further, the phrase “based on” is intended to mean “based at least partially on” unless explicitly stated otherwise.

Referring now toFIG.1, illustrated is example environment100in which vehicles that include autonomous systems, as well as vehicles that do not, are operated. As illustrated, environment100includes vehicles102a-102n, objects104a-104n, routes106a-106n, area108, vehicle-to-infrastructure (V2I) device110, network112, remote autonomous vehicle (AV) system114, fleet management system116, and V2I system118. Vehicles102a-102n, vehicle-to-infrastructure (V2I) device110, network112, autonomous vehicle (AV) system114, fleet management system116, and V2I system118interconnect (e.g., establish a connection to communicate and/or the like) via wired connections, wireless connections, or a combination of wired or wireless connections. In some embodiments, objects104a-104ninterconnect with at least one of vehicles102a-102n, vehicle-to-infrastructure (V2I) device110, network112, autonomous vehicle (AV) system114, fleet management system116, and V2I system118via wired connections, wireless connections, or a combination of wired or wireless connections.

Vehicles102a-102n(referred to individually as vehicle102and collectively as vehicles102) include at least one device configured to transport goods and/or people. In some embodiments, vehicles102are configured to be in communication with V2I device110, remote AV system114, fleet management system116, and/or V2I system118via network112. In some embodiments, vehicles102include cars, buses, trucks, trains, and/or the like. In some embodiments, vehicles102are the same as, or similar to, vehicles200, described herein (seeFIG.2). In some embodiments, a vehicle200of a set of vehicles200is associated with an autonomous fleet manager. In some embodiments, vehicles102travel along respective routes106a-106n(referred to individually as route106and collectively as routes106), as described herein. In some embodiments, one or more vehicles102include an autonomous system (e.g., an autonomous system that is the same as or similar to autonomous system202).

Objects104a-104n(referred to individually as object104and collectively as objects104) include, for example, at least one vehicle, at least one pedestrian, at least one cyclist, at least one structure (e.g., a building, a sign, a fire hydrant, etc.), and/or the like. Each object104is stationary (e.g., located at a fixed location for a period of time) or mobile (e.g., having a velocity and associated with at least one trajectory). In some embodiments, objects104are associated with corresponding locations in area108.

Routes106a-106n(referred to individually as route106and collectively as routes106) are each associated with (e.g., prescribe) a sequence of actions (also known as a trajectory) connecting states along which an AV can navigate. Each route106starts at an initial state (e.g., a state that corresponds to a first spatiotemporal location, velocity, and/or the like) and a final goal state (e.g., a state that corresponds to a second spatiotemporal location that is different from the first spatiotemporal location) or goal region (e.g. a subspace of acceptable states (e.g., terminal states)). In some embodiments, the first state includes a location at which an individual or individuals are to be picked-up by the AV and the second state or region includes a location or locations at which the individual or individuals picked-up by the AV are to be dropped-off. In some embodiments, routes106include a plurality of acceptable state sequences (e.g., a plurality of spatiotemporal location sequences), the plurality of state sequences associated with (e.g., defining) a plurality of trajectories. In an example, routes106include only high level actions or imprecise state locations, such as a series of connected roads dictating turning directions at roadway intersections. Additionally, or alternatively, routes106may include more precise actions or states such as, for example, specific target lanes or precise locations within the lane areas and targeted speed at those positions. In an example, routes106include a plurality of precise state sequences along the at least one high level action sequence with a limited lookahead horizon to reach intermediate goals, where the combination of successive iterations of limited horizon state sequences cumulatively correspond to a plurality of trajectories that collectively form the high level route to terminate at the final goal state or region.

Area108includes a physical area (e.g., a geographic region) within which vehicles102can navigate. In an example, area108includes at least one state (e.g., a country, a province, an individual state of a plurality of states included in a country, etc.), at least one portion of a state, at least one city, at least one portion of a city, etc. In some embodiments, area108includes at least one named thoroughfare (referred to herein as a “road”) such as a highway, an interstate highway, a parkway, a city street, etc. Additionally, or alternatively, in some examples area108includes at least one unnamed road such as a driveway, a section of a parking lot, a section of a vacant and/or undeveloped lot, a dirt path, etc. In some embodiments, a road includes at least one lane (e.g., a portion of the road that can be traversed by vehicles102). In an example, a road includes at least one lane associated with (e.g., identified based on) at least one lane marking.

Vehicle-to-Infrastructure (V2I) device110(sometimes referred to as a Vehicle-to-Infrastructure (V2X) device) includes at least one device configured to be in communication with vehicles102and/or V2I infrastructure system118. In some embodiments, V2I device110is configured to be in communication with vehicles102, remote AV system114, fleet management system116, and/or V2I system118via network112. In some embodiments, V2I device110includes a radio frequency identification (RFID) device, signage, cameras (e.g., two-dimensional (2D) and/or three-dimensional (3D) cameras), lane markers, streetlights, parking meters, etc. In some embodiments, V2I device110is configured to communicate directly with vehicles102. Additionally, or alternatively, in some embodiments V2I device110is configured to communicate with vehicles102, remote AV system114, and/or fleet management system116via V2I system118. In some embodiments, V2I device110is configured to communicate with V2I system118via network112.

Network112includes one or more wired and/or wireless networks. In an example, network112includes a cellular network (e.g., a long term evolution (LTE) network, a third generation (3G) network, a fourth generation (4G) network, a fifth generation (5G) network, a code division multiple access (CDMA) network, etc.), a public land mobile network (PLMN), a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), a telephone network (e.g., the public switched telephone network (PSTN), a private network, an ad hoc network, an intranet, the Internet, a fiber optic-based network, a cloud computing network, etc., a combination of some or all of these networks, and/or the like.

Remote AV system114includes at least one device configured to be in communication with vehicles102, V2I device110, network112, remote AV system114, fleet management system116, and/or V2I system118via network112. In an example, remote AV system114includes a server, a group of servers, and/or other like devices. In some embodiments, remote AV system114is co-located with the fleet management system116. In some embodiments, remote AV system114is involved in the installation of some or all of the components of a vehicle, including an autonomous system, an autonomous vehicle compute, software implemented by an autonomous vehicle compute, and/or the like. In some embodiments, remote AV system114maintains (e.g., updates and/or replaces) such components and/or software during the lifetime of the vehicle.

Fleet management system116includes at least one device configured to be in communication with vehicles102, V2I device110, remote AV system114, and/or V2I infrastructure system118. In an example, fleet management system116includes a server, a group of servers, and/or other like devices. In some embodiments, fleet management system116is associated with a ridesharing company (e.g., an organization that controls operation of multiple vehicles (e.g., vehicles that include autonomous systems and/or vehicles that do not include autonomous systems) and/or the like).

In some embodiments, V2I system118includes at least one device configured to be in communication with vehicles102, V2I device110, remote AV system114, and/or fleet management system116via network112. In some examples, V2I system118is configured to be in communication with V2I device110via a connection different from network112. In some embodiments, V2I system118includes a server, a group of servers, and/or other like devices. In some embodiments, V2I system118is associated with a municipality or a private institution (e.g., a private institution that maintains V2I device110and/or the like).

The number and arrangement of elements illustrated inFIG.1are provided as an example. There can be additional elements, fewer elements, different elements, and/or differently arranged elements, than those illustrated inFIG.1. Additionally, or alternatively, at least one element of environment100can perform one or more functions described as being performed by at least one different element ofFIG.1. Additionally, or alternatively, at least one set of elements of environment100can perform one or more functions described as being performed by at least one different set of elements of environment100.

Referring now toFIG.2, vehicle200includes autonomous system202, powertrain control system204, steering control system206, and brake system208. In some embodiments, vehicle200is the same as or similar to vehicle102(seeFIG.1). In some embodiments, vehicle102have autonomous capability (e.g., implement at least one function, feature, device, and/or the like that enable vehicle200to be partially or fully operated without human intervention including, without limitation, fully autonomous vehicles (e.g., vehicles that forego reliance on human intervention), highly autonomous vehicles (e.g., vehicles that forego reliance on human intervention in certain situations), and/or the like). For a detailed description of fully autonomous vehicles and highly autonomous vehicles, reference may be made to SAE International's standard J3016: Taxonomy and Definitions for Terms Related to On-Road Motor Vehicle Automated Driving Systems, which is incorporated by reference in its entirety. In some embodiments, vehicle200is associated with an autonomous fleet manager and/or a ridesharing company.

Autonomous system202includes a sensor suite that includes one or more devices such as cameras202a, LiDAR sensors202b, radar sensors202c, and microphones202d. In some embodiments, autonomous system202can include more or fewer devices and/or different devices (e.g., ultrasonic sensors, inertial sensors, GPS receivers (discussed below), odometry sensors that generate data associated with an indication of a distance that vehicle200has traveled, and/or the like). In some embodiments, autonomous system202uses the one or more devices included in autonomous system202to generate data associated with environment100, described herein. The data generated by the one or more devices of autonomous system202can be used by one or more systems described herein to observe the environment (e.g., environment100) in which vehicle200is located. In some embodiments, autonomous system202includes communication device202e, autonomous vehicle compute202f, and drive-by-wire (DBW) system202h.

Cameras202ainclude at least one device configured to be in communication with communication device202e, autonomous vehicle compute202f, and/or safety controller202gvia a bus (e.g., a bus that is the same as or similar to bus302ofFIG.3). Cameras202ainclude at least one camera (e.g., a digital camera using a light sensor such as a charge-coupled device (CCD), a thermal camera, an infrared (IR) camera, an event camera, and/or the like) to capture images including physical objects (e.g., cars, buses, curbs, people, and/or the like). In some embodiments, camera202agenerates camera data as output. In some examples, camera202agenerates camera data that includes image data associated with an image. In this example, the image data may specify at least one parameter (e.g., image characteristics such as exposure, brightness, etc., an image timestamp, and/or the like) corresponding to the image. In such an example, the image may be in a format (e.g., RAW, JPEG, PNG, and/or the like). In some embodiments, camera202aincludes a plurality of independent cameras configured on (e.g., positioned on) a vehicle to capture images for the purpose of stereopsis (stereo vision). In some examples, camera202aincludes a plurality of cameras that generate image data and transmit the image data to autonomous vehicle compute202fand/or a fleet management system (e.g., a fleet management system that is the same as or similar to fleet management system116ofFIG.1). In such an example, autonomous vehicle compute202fdetermines depth to one or more objects in a field of view of at least two cameras of the plurality of cameras based on the image data from the at least two cameras. In some embodiments, cameras202aare configured to capture images of objects within a distance from cameras202a(e.g., up to 100 meters, up to a kilometer, and/or the like). Accordingly, cameras202ainclude features such as sensors and lenses that are optimized for perceiving objects that are at one or more distances from cameras202a.

In an embodiment, camera202aincludes at least one camera configured to capture one or more images associated with one or more traffic lights, street signs and/or other physical objects that provide visual navigation information. In some embodiments, camera202agenerates traffic light data associated with one or more images. In some examples, camera202agenerates TLD data associated with one or more images that include a format (e.g., RAW, JPEG, PNG, and/or the like). In some embodiments, camera202athat generates TLD data differs from other systems described herein incorporating cameras in that camera202acan include one or more cameras with a wide field of view (e.g., a wide-angle lens, a fish-eye lens, a lens having a viewing angle of approximately 120 degrees or more, and/or the like) to generate images about as many physical objects as possible.

LiDAR sensors202binclude at least one device configured to be in communication with communication device202e, autonomous vehicle compute202f, and/or safety controller202gvia a bus (e.g., a bus that is the same as or similar to bus302ofFIG.3). LiDAR sensors202binclude a system configured to transmit light from a light emitter (e.g., a laser transmitter). Light emitted by LiDAR sensors202binclude light (e.g., infrared light and/or the like) that is outside of the visible spectrum. In some embodiments, during operation, light emitted by LiDAR sensors202bencounters a physical object (e.g., a vehicle) and is reflected back to LiDAR sensors202b. In some embodiments, the light emitted by LiDAR sensors202bdoes not penetrate the physical objects that the light encounters. LiDAR sensors202balso include at least one light detector which detects the light that was emitted from the light emitter after the light encounters a physical object. In some embodiments, at least one data processing system associated with LiDAR sensors202bgenerates an image (e.g., a point cloud, a combined point cloud, and/or the like) representing the objects included in a field of view of LiDAR sensors202b. In some examples, the at least one data processing system associated with LiDAR sensor202bgenerates an image that represents the boundaries of a physical object, the surfaces (e.g., the topology of the surfaces) of the physical object, and/or the like. In such an example, the image is used to determine the boundaries of physical objects in the field of view of LiDAR sensors202b.

Radio Detection and Ranging (radar) sensors202cinclude at least one device configured to be in communication with communication device202e, autonomous vehicle compute202f, and/or safety controller202gvia a bus (e.g., a bus that is the same as or similar to bus302ofFIG.3). Radar sensors202cinclude a system configured to transmit radio waves (either pulsed or continuously). The radio waves transmitted by radar sensors202cinclude radio waves that are within a predetermined spectrum. In some embodiments, during operation, radio waves transmitted by radar sensors202cencounter a physical object and are reflected back to radar sensors202c. In some embodiments, the radio waves transmitted by radar sensors202care not reflected by some objects. In some embodiments, at least one data processing system associated with radar sensors202cgenerates signals representing the objects included in a field of view of radar sensors202c. For example, the at least one data processing system associated with radar sensor202cgenerates an image that represents the boundaries of a physical object, the surfaces (e.g., the topology of the surfaces) of the physical object, and/or the like. In some examples, the image is used to determine the boundaries of physical objects in the field of view of radar sensors202c.

Microphones202dincludes at least one device configured to be in communication with communication device202e, autonomous vehicle compute202f, and/or safety controller202gvia a bus (e.g., a bus that is the same as or similar to bus302ofFIG.3). Microphones202dinclude one or more microphones (e.g., array microphones, external microphones, and/or the like) that capture audio signals and generate data associated with (e.g., representing) the audio signals. In some examples, microphones202dinclude transducer devices and/or like devices. In some embodiments, one or more systems described herein can receive the data generated by microphones202dand determine a position of an object relative to vehicle200(e.g., a distance and/or the like) based on the audio signals associated with the data.

Communication device202einclude at least one device configured to be in communication with cameras202a, LiDAR sensors202b, radar sensors202c, microphones202d, autonomous vehicle compute202f, safety controller202g, and/or DBW system202h. For example, communication device202emay include a device that is the same as or similar to communication interface314ofFIG.3. In some embodiments, communication device202eincludes a vehicle-to-vehicle (V2V) communication device (e.g., a device that enables wireless communication of data between vehicles).

Autonomous vehicle compute202finclude at least one device configured to be in communication with cameras202a, LiDAR sensors202b, radar sensors202c, microphones202d, communication device202e, safety controller202g, and/or DBW system202h. In some examples, autonomous vehicle compute202fincludes a device such as a client device, a mobile device (e.g., a cellular telephone, a tablet, and/or the like) a server (e.g., a computing device including one or more central processing units, graphical processing units, and/or the like), and/or the like. In some embodiments, autonomous vehicle compute202fis the same as or similar to autonomous vehicle compute400, described herein. Additionally, or alternatively, in some embodiments autonomous vehicle compute202fis configured to be in communication with an autonomous vehicle system (e.g., an autonomous vehicle system that is the same as or similar to remote AV system114ofFIG.1), a fleet management system (e.g., a fleet management system that is the same as or similar to fleet management system116ofFIG.1), a V2I device (e.g., a V2I device that is the same as or similar to V2I device110ofFIG.1), and/or a V2I system (e.g., a V2I system that is the same as or similar to V2I system118ofFIG.1).

Safety controller202gincludes at least one device configured to be in communication with cameras202a, LiDAR sensors202b, radar sensors202c, microphones202d, communication device202e, autonomous vehicle computer202f, and/or DBW system202h. In some examples, safety controller202gincludes one or more controllers (electrical controllers, electromechanical controllers, and/or the like) that are configured to generate and/or transmit control signals to operate one or more devices of vehicle200(e.g., powertrain control system204, steering control system206, brake system208, and/or the like). In some embodiments, safety controller202gis configured to generate control signals that take precedence over (e.g., overrides) control signals generated and/or transmitted by autonomous vehicle compute202f.

DBW system202hincludes at least one device configured to be in communication with communication device202eand/or autonomous vehicle compute202f. In some examples, DBW system202hincludes one or more controllers (e.g., electrical controllers, electromechanical controllers, and/or the like) that are configured to generate and/or transmit control signals to operate one or more devices of vehicle200(e.g., powertrain control system204, steering control system206, brake system208, and/or the like). Additionally, or alternatively, the one or more controllers of DBW system202hare configured to generate and/or transmit control signals to operate at least one different device (e.g., a turn signal, headlights, door locks, windshield wipers, and/or the like) of vehicle200.

Powertrain control system204includes at least one device configured to be in communication with DBW system202h. In some examples, powertrain control system204includes at least one controller, actuator, and/or the like. In some embodiments, powertrain control system204receives control signals from DBW system202hand powertrain control system204causes vehicle200to start moving forward, stop moving forward, start moving backward, stop moving backward, accelerate in a direction, decelerate in a direction, perform a left turn, perform a right turn, and/or the like. In an example, powertrain control system204causes the energy (e.g., fuel, electricity, and/or the like) provided to a motor of the vehicle to increase, remain the same, or decrease, thereby causing at least one wheel of vehicle200to rotate or not rotate.

Steering control system206includes at least one device configured to rotate one or more wheels of vehicle200. In some examples, steering control system206includes at least one controller, actuator, and/or the like. In some embodiments, steering control system206causes the front two wheels and/or the rear two wheels of vehicle200to rotate to the left or right to cause vehicle200to turn to the left or right.

Brake system208includes at least one device configured to actuate one or more brakes to cause vehicle200to reduce speed and/or remain stationary. In some examples, brake system208includes at least one controller and/or actuator that is configured to cause one or more calipers associated with one or more wheels of vehicle200to close on a corresponding rotor of vehicle200. Additionally, or alternatively, in some examples brake system208includes an automatic emergency braking (AEB) system, a regenerative braking system, and/or the like.

In some embodiments, vehicle200includes at least one platform sensor (not explicitly illustrated) that measures or infers properties of a state or a condition of vehicle200. In some examples, vehicle200includes platform sensors such as a global positioning system (GPS) receiver, an inertial measurement unit (IMU), a wheel speed sensor, a wheel brake pressure sensor, a wheel torque sensor, an engine torque sensor, a steering angle sensor, and/or the like.

Referring now toFIG.3, illustrated is a schematic diagram of a device300. As illustrated, device300includes processor304, memory306, storage component308, input interface310, output interface312, communication interface314, and bus302. In some embodiments, device300corresponds to at least one device of vehicles102(e.g., at least one device of a system of vehicles102), at least one device of [continue list in similar manner for all devices contemplated inFIGS.1-3], and/or one or more devices of network112(e.g., one or more devices of a system of network112). In some embodiments, one or more devices of vehicles102(e.g., one or more devices of a system of vehicles102), and/or one or more devices of network112(e.g., one or more devices of a system of network112) include at least one device300and/or at least one component of device300. As shown inFIG.3, device300includes bus302, processor304, memory306, storage component308, input interface310, output interface312, and communication interface314.

Bus302includes a component that permits communication among the components of device300. In some embodiments, processor304is implemented in hardware, software, or a combination of hardware and software. In some examples, processor304includes a processor (e.g., a central processing unit (CPU), a graphics processing unit (GPU), an accelerated processing unit (APU), and/or the like), a microphone, a digital signal processor (DSP), and/or any processing component (e.g., a field-programmable gate array (FPGA), an application specific integrated circuit (ASIC), and/or the like) that can be programmed to perform at least one function. Memory306includes random access memory (RAM), read-only memory (ROM), and/or another type of dynamic and/or static storage device (e.g., flash memory, magnetic memory, optical memory, and/or the like) that stores data and/or instructions for use by processor304.

Storage component308stores data and/or software related to the operation and use of device300. In some examples, storage component308includes a hard disk (e.g., a magnetic disk, an optical disk, a magneto-optic disk, a solid state disk, and/or the like), a compact disc (CD), a digital versatile disc (DVD), a floppy disk, a cartridge, a magnetic tape, a CD-ROM, RAM, PROM, EPROM, FLASH-EPROM, NV-RAM, and/or another type of computer readable medium, along with a corresponding drive.

Input interface310includes a component that permits device300to receive information, such as via user input (e.g., a touchscreen display, a keyboard, a keypad, a mouse, a button, a switch, a microphone, a camera, and/or the like). Additionally or alternatively, in some embodiments input interface310includes a sensor that senses information (e.g., a global positioning system (GPS) receiver, an accelerometer, a gyroscope, an actuator, and/or the like). Output interface312includes a component that provides output information from device300(e.g., a display, a speaker, one or more light-emitting diodes (LEDs), and/or the like).

In some embodiments, communication interface314includes a transceiver-like component (e.g., a transceiver, a separate receiver and transmitter, and/or the like) that permits device300to communicate with other devices via a wired connection, a wireless connection, or a combination of wired and wireless connections. In some examples, communication interface314permits device300to receive information from another device and/or provide information to another device. In some examples, communication interface314includes an Ethernet interface, an optical interface, a coaxial interface, an infrared interface, a radio frequency (RF) interface, a universal serial bus (USB) interface, a Wi-Fi® interface, a cellular network interface, and/or the like.

In some embodiments, device300performs one or more processes described herein. Device300performs these processes based on processor304executing software instructions stored by a computer-readable medium, such as memory305and/or storage component308. A computer-readable medium (e.g., a non-transitory computer readable medium) is defined herein as a non-transitory memory device. A non-transitory memory device includes memory space located inside a single physical storage device or memory space spread across multiple physical storage devices.

In some embodiments, software instructions are read into memory306and/or storage component308from another computer-readable medium or from another device via communication interface314. When executed, software instructions stored in memory306and/or storage component308cause processor304to perform one or more processes described herein. Additionally or alternatively, hardwired circuitry is used in place of or in combination with software instructions to perform one or more processes described herein. Thus, embodiments described herein are not limited to any specific combination of hardware circuitry and software unless explicitly stated otherwise.

Memory306and/or storage component308includes data storage or at least one data structure (e.g., a database and/or the like). Device300is capable of receiving information from, storing information in, communicating information to, or searching information stored in the data storage or the at least one data structure in memory306or storage component308. In some examples, the information includes network data, input data, output data, or any combination thereof.

In some embodiments, device300is configured to execute software instructions that are either stored in memory306and/or in the memory of another device (e.g., another device that is the same as or similar to device300). As used herein, the term “module” refers to at least one instruction stored in memory306and/or in the memory of another device that, when executed by processor304and/or by a processor of another device (e.g., another device that is the same as or similar to device300) cause device300(e.g., at least one component of device300) to perform one or more processes described herein. In some embodiments, a module is implemented in software, firmware, hardware, and/or the like.

The number and arrangement of components illustrated inFIG.3are provided as an example. In some embodiments, device300can include additional components, fewer components, different components, or differently arranged components than those illustrated inFIG.3. Additionally or alternatively, a set of components (e.g., one or more components) of device300can perform one or more functions described as being performed by another component or another set of components of device300.

Referring now toFIG.4, illustrated is an example block diagram of an autonomous vehicle compute400(sometimes referred to as an “AV stack”). As illustrated, autonomous vehicle compute400includes perception system402(sometimes referred to as a perception module), planning system404(sometimes referred to as a planning module), localization system406(sometimes referred to as a localization module), control system408(sometimes referred to as a control module), and database410. In some embodiments, perception system402, planning system404, localization system406, control system408, and database410are included and/or implemented in an autonomous navigation system of a vehicle (e.g., autonomous vehicle compute202fof vehicle200). Additionally, or alternatively, in some embodiments perception system402, planning system404, localization system406, control system408, and database410are included in one or more standalone systems (e.g., one or more systems that are the same as or similar to autonomous vehicle compute400and/or the like). In some examples, perception system402, planning system404, localization system406, control system408, and database410are included in one or more standalone systems that are located in a vehicle and/or at least one remote system as described herein. In some embodiments, any and/or all of the systems included in autonomous vehicle compute400are implemented in software (e.g., in software instructions stored in memory), computer hardware (e.g., by microprocessors, microcontrollers, application-specific integrated circuits [ASICs], Field Programmable Gate Arrays (FPGAs), and/or the like), or combinations of computer software and computer hardware. It will also be understood that, in some embodiments, autonomous vehicle compute400is configured to be in communication with a remote system (e.g., an autonomous vehicle system that is the same as or similar to remote AV system114, a fleet management system116that is the same as or similar to fleet management system116, a V2I system that is the same as or similar to V2I system118, and/or the like).

In some embodiments, perception system402receives data associated with at least one physical object (e.g., data that is used by perception system402to detect the at least one physical object) in an environment and classifies the at least one physical object. In some examples, perception system402receives image data captured by at least one camera (e.g., cameras202a), the image associated with (e.g., representing) one or more physical objects within a field of view of the at least one camera. In such an example, perception system402classifies at least one physical object based on one or more groupings of physical objects (e.g., bicycles, vehicles, traffic signs, pedestrians, and/or the like). In some embodiments, perception system402transmits data associated with the classification of the physical objects to planning system404based on perception system402classifying the physical objects.

In some embodiments, planning system404receives data associated with a destination and generates data associated with at least one route (e.g., routes106) along which a vehicle (e.g., vehicles102) can travel along toward a destination. In some embodiments, planning system404periodically or continuously receives data from perception system402(e.g., data associated with the classification of physical objects, described above) and planning system404updates the at least one trajectory or generates at least one different trajectory based on the data generated by perception system402. In some embodiments, planning system404receives data associated with an updated position of a vehicle (e.g., vehicles102) from localization system406and planning system404updates the at least one trajectory or generates at least one different trajectory based on the data generated by localization system406.

In some embodiments, localization system406receives data associated with (e.g., representing) a location of a vehicle (e.g., vehicles102) in an area. In some examples, localization system406receives LiDAR data associated with at least one point cloud generated by at least one LiDAR sensor (e.g., LiDAR sensors202b). In certain examples, localization system406receives data associated with at least one point cloud from multiple LiDAR sensors and localization system406generates a combined point cloud based on each of the point clouds. In these examples, localization system406compares the at least one point cloud or the combined point cloud to two-dimensional (2D) and/or a three-dimensional (3D) map of the area stored in database410. Localization system406then determines the position of the vehicle in the area based on localization system406comparing the at least one point cloud or the combined point cloud to the map. In some embodiments, the map includes a combined point cloud of the area generated prior to navigation of the vehicle. In some embodiments, maps include, without limitation, high-precision maps of the roadway geometric properties, maps describing road network connectivity properties, maps describing roadway physical properties (such as traffic speed, traffic volume, the number of vehicular and cyclist traffic lanes, lane width, lane traffic directions, or lane marker types and locations, or combinations thereof), and maps describing the spatial locations of road features such as crosswalks, traffic signs or other travel signals of various types. In some embodiments, the map is generated in real-time based on the data received by the perception system.

In another example, localization system406receives Global Navigation Satellite System (GNSS) data generated by a global positioning system (GPS) receiver. In some examples, localization system406receives GNSS data associated with the location of the vehicle in the area and localization system406determines a latitude and longitude of the vehicle in the area. In such an example, localization system406determines the position of the vehicle in the area based on the latitude and longitude of the vehicle. In some embodiments, localization system406generates data associated with the position of the vehicle. In some examples, localization system406generates data associated with the position of the vehicle based on localization system406determining the position of the vehicle. In such an example, the data associated with the position of the vehicle includes data associated with one or more semantic properties corresponding to the position of the vehicle.

In some embodiments, control system408receives data associated with at least one trajectory from planning system404and control system408controls operation of the vehicle. In some examples, control system408receives data associated with at least one trajectory from planning system404and control system408controls operation of the vehicle by generating and transmitting control signals to cause a powertrain control system (e.g., DBW system202h, powertrain control system204, and/or the like), a steering control system (e.g., steering control system206), and/or a brake system (e.g., brake system208) to operate. In an example, where a trajectory includes a left turn, control system408transmits a control signal to cause steering control system206to adjust a steering angle of vehicle200, thereby causing vehicle200to turn left. Additionally, or alternatively, control system408generates and transmits control signals to cause other devices (e.g., headlights, turn signal, door locks, windshield wipers, and/or the like) of vehicle200to change states.

In some embodiments, perception system402, planning system404, localization system406, and/or control system408implement at least one machine learning model (e.g., at least one multilayer perceptron (MLP), at least one convolutional neural network (CNN), at least one recurrent neural network (RNN), at least one autoencoder, at least one transformer, and/or the like). In some examples, perception system402, planning system404, localization system406, and/or control system408implement at least one machine learning model alone or in combination with one or more of the above-noted systems. In some examples, perception system402, planning system404, localization system406, and/or control system408implement at least one machine learning model as part of a pipeline (e.g., a pipeline for identifying one or more objects located in an environment and/or the like).

Database410stores data that is transmitted to, received from, and/or updated by perception system402, planning system404, localization system406and/or control system408. In some examples, database410includes a storage component (e.g., a storage component that is the same as or similar to storage component308ofFIG.3) that stores data and/or software related to the operation and uses at least one system of autonomous vehicle compute400. In some embodiments, database410stores data associated with 2D and/or 3D maps of at least one area. In some examples, database410stores data associated with 2D and/or 3D maps of a portion of a city, multiple portions of multiple cities, multiple cities, a county, a state, a State (e.g., a country), and/or the like). In such an example, a vehicle (e.g., a vehicle that is the same as or similar to vehicles102and/or vehicle200) can drive along one or more drivable regions (e.g., single-lane roads, multi-lane roads, highways, back roads, off road trails, and/or the like) and cause at least one LiDAR sensor (e.g., a LiDAR sensor that is the same as or similar to LiDAR sensors202b) to generate data associated with an image representing the objects included in a field of view of the at least one LiDAR sensor.

In some embodiments, database410can be implemented across a plurality of devices. In some examples, database410is included in a vehicle (e.g., a vehicle that is the same as or similar to vehicles102and/or vehicle200), an autonomous vehicle system (e.g., an autonomous vehicle system that is the same as or similar to remote AV system114, a fleet management system (e.g., a fleet management system that is the same as or similar to fleet management system116ofFIG.1, a V2I system (e.g., a V2I system that is the same as or similar to V2I system118ofFIG.1) and/or the like.

Referring now toFIG.5, illustrated is a diagram of an implementation of a process for camera alignment using pre-distorted targets. In some embodiments, this implementation may be carried out in alignment processes for any of cameras202a. As shown inFIG.5, an alignment process is performed by alignment system500, to align one or more of cameras202a. In this process, a projector502projects an image508for detection by cameras202a. Projector502may be any device capable of projecting an image suitable for detection by cameras202a, such as a display device, e.g., a monitor, television, other flat panel display, or the like. Image508includes an image of a number of shapes510which have been pre-distorted to produce specified shapes when viewed through a particular lens system. For example, image508may include a number of shapes510that are pre-distorted to produce shapes when viewed through a lens system such that the projection of shapes510onto the lenses of cameras202aresults in certain desired shapes at certain desired locations within images captured by cameras202a. In an example, shapes510are calculated to produce an array of rectangles in the images generated by cameras202a. Embodiments of the disclosure contemplate any desired shape generated in images of cameras202a, where the shapes510are those shapes which are calculated to generate these desired shapes in the images produced by cameras202a.

In operation, projector502projects image508upon the lenses of cameras202a. The images508each contain shapes510which are determined such that, when passed through lenses shaped according to the nominal or specified dimensions of the lenses of cameras202a, predetermined shapes are generated in the images output by cameras202a. As one example, the shapes510are calculated and selected such that, when viewed by theoretical lenses having the nominal dimensions of the lenses of cameras202a, images output by cameras202acontain particular desired shapes such as rectangles. That is, while any desired shape is contemplated for the images output by cameras202a, shapes510are determined such that they generate those desired shapes within the images output by cameras202a.

Processor506then directs alignment actuators504, which are operably coupled to the lenses of cameras202a, to manipulate the orientations of the lenses of cameras202asuch that the resulting shapes in camera202aimages correspond closely in shape and location to the desired shapes. That is, the alignment actuators504align the lenses of cameras202asuch that the images of shapes510within the images output by cameras202aalign closely with the desired shapes and their locations. Close alignment indicates that the lenses and sensor arrays of cameras202aare properly aligned, whereupon the principal points and effective focal lengths of cameras202aare determined as described below, facilitating more accurate calibration.

FIG.6illustrates the process ofFIG.5in further detail. An image508is displayed for detection by cameras202a, where the image508contains a number of shapes510. The shapes510are chosen such that, when projected through a theoretical or nominal lens or lenses having the dimensions and properties specified for the lens(es) of cameras202a, a predetermined shape is generated in the resulting image. For example, shapes510may be selected such that they appear as rectangles604in image600produced by cameras202a. More specifically, shapes510may be selected to produce a specified array of rectangles604of particular dimensions, when projected through hypothetical, perfectly aligned lenses having the dimensions specified for lenses of cameras202a. The actual shapes602which appear in image600are then compared to the rectangles604that would be generated by an ideal, perfectly shaped and aligned lens, with alignment system500performing lens alignment using the degree of correspondence between the two as feedback.

In this manner, a pattern of shapes604useful in performing camera alignment is first selected. From this pattern, shapes510are determined as those shapes which generate the pattern of shapes604when viewed through theoretical or nominal lenses of cameras202a. That is, shapes510are those shapes which produce shapes604when distorted by theoretical or nominal lenses of cameras202a. Accordingly, embodiments of the disclosure employ shapes510that are pre-distorted so as to produce regular or otherwise desired shapes604when subjected to distortion by the lenses of cameras202awhich they are intended to align. Embodiments of the disclosure thus generate pre-distorted images or shapes510specific to each lens system, for aligning that lens system by comparison of the resulting images to the theoretical image those shapes would produce if that lens system were perfectly aligned.

While shapes604are shown inFIG.6as rectangles, it is noted that embodiments of the disclosure contemplate any geometry of shapes604without limitation. For example, shapes604may be squares, any polygon or multi-sided shapes, circles, ovals, or the like. In some embodiments of the disclosure, shapes604include at least some slanted, straight sides, for facilitating modulation transfer function (MTF) calculation. Furthermore, embodiments of the disclosure contemplate any number and arrangement of shapes604, which may each be the same shape or may differ from one another in any manner.

Alignment system500may be any system for performing an alignment process on a camera, such as an active alignment station capable of manipulating camera lenses or other components for centering, alignment, and the like. Alignment system500aligns lenses, sensor arrays, or other camera components according to the degree of alignment between ideal rectangles604and the shapes602that actually appear in images600. That is, using images600as feedback, alignment system500manipulates various camera202acomponents, such as lenses and/or sensor arrays, until shapes602align as closely as desired with the sizes, shapes, and positions of rectangles604. Camera202acomponents are then deemed aligned, or as closely aligned as desired.

Referring now toFIG.7, illustrated is a flowchart of a process700for camera alignment using pre-distorted targets. In some embodiments, one or more of the steps described with respect to process700are performed (e.g., completely, partially, and/or the like) while cameras202aare installed in autonomous vehicle102, as above. Additionally or alternatively, in some embodiments one or more steps described with respect to process700are performed (e.g., completely, partially, and/or the like) on cameras202awhile cameras202aare located remote from autonomous vehicle102, such as prior to installation of cameras202ain autonomous vehicle102. Furthermore, pre-distorted shapes510may be determined at any time prior to alignment of cameras202a, or may be determined by, e.g., alignment system500, during camera alignment.

Initially, alignment system500determines at least one first shape (e.g., pre-distorted shapes510) selected to generate at least one predetermined second shape (e.g., rectangles604) in an image of a camera202awhen the at least one first shape is projected through a lens of camera202aonto a sensor or sensor array of camera202a(Step702). As above, alignment system500selects an arrangement of one or more second shapes, such as rectangles604, which lend themselves well to alignment or other processes. For example, an array of rectangles604may be chosen, although any distribution of any shapes may be selected. Alignment system500then calculates the shapes510which will produce rectangles604in image600after distortion by the lenses of cameras202a. That is, alignment system500selects a desired pattern in an output image600of a camera, and calculates those shapes which should produce this desired pattern when projected through the nominal lens system of that camera.

Next, alignment system500projects, or causes projection of, the at least one first shape onto the lens of camera202a, to facilitate alignment of the lens of camera202awith the sensor array of camera202a(Step704). For example, alignment system500generates image508of shapes510on projector502, to project image508upon cameras202a. Cameras202athen generate one or more images600captured by their sensor arrays, and transmit these images600to alignment system500. Alignment system500then compares the projections of the at least one first shape (e.g., shapes602) in image600to the at least one second shape (e.g., rectangles604), to determine differences between the two (Step706). For example, alignment system500may superimpose an image of rectangles604upon an image600of shapes602, as shown inFIG.6, to visually display differences between the two. Alignment system500then causes alignment of the lens of camera202awith its sensor array, according to the difference between shapes602and rectangles604(Step708). As an example, alignment system500automatically manipulates lenses and/or sensor arrays of cameras202avia alignment actuators504, to reduce or minimize differences in size, shape, and/or position between shapes602and rectangles604. Alternatively, or in addition, alignment system500may allow manual manipulation of lenses and/or sensor arrays of cameras202avia alignment actuators504, to reduce or minimize differences in size, shape, and/or position between shapes602and rectangles604according to operator perception and input. During this process, cameras202amay output multiple images600, such as in real time or substantial real time, to provide continuous feedback for alignment processes.

Once alignment system500has sufficiently aligned the lenses and sensor arrays of cameras202a, system500further determines other optical properties of camera202a, and performs other desired processes on camera202a. In an example, image508includes a number of shapes510that are pre-distorted to produce shapes when viewed through a lens system such that the projection of shapes510onto the lenses of cameras202aresults in certain desired shapes at certain desired locations within images captured by cameras202a. In such an example, alignment system500determines the principal point of camera202abased on the geometric center of projections of the at least one first shape in image600, e.g., the geometric center of shapes602(Step710). That is, in embodiments of the disclosure, the principal point of camera202ais determined as the geometric center of the pattern of shapes602resulting from display of pre-distorted shapes510, when the camera202ais sufficiently aligned.

Once the principal point is determined, system500causes projection of an additional shape onto the lens of camera202a, at a position within its output image600corresponding to the principal point (Step712). That is, an additional shape is added to image508at the determined principal point. Embodiments of the disclosure contemplate use of any shape for use as this additional shape.

One of ordinary skill in the art will realize that, principal point having been determined, various other optical properties of camera202amay further be determined, such as effective focal length and MTF. One of ordinary skill in the art will also realize that processes such as calibration may also be carried out for camera202a. Accordingly, in the process ofFIG.7, alignment system500determines the effective focal length of the lens system of camera202aat least in part from this additional shape, and further determines the MTF of the camera202alens system at least in part from edges of at least one of the first shapes (e.g., shapes602) of image600, as well as perform or cause to be performed a calibration process for camera202a(Step714).

In this manner, principal points are determined in simpler and more accurate manner. Additionally, determined principal points may be employed as initial guesses in camera calibration processes, leading to more accurate calibration resulting from more accurate initial principal point estimates.

FIG.8is a flowchart illustrating further details of Step702above, for determining pre-distorted shapes510. As described above, in an example, image508includes a number of shapes510that are pre-distorted to produce shapes when viewed through a lens system such that the projection of shapes510onto the lenses of cameras202aresults in certain desired shapes at certain desired locations within images captured by cameras202a. Desired shapes604are determined first, and pre-distorted shapes510are then determined from those desired shapes604and the specified dimensions of the lenses to be used, e.g., the nominal or theoretical lenses based upon which the actual lenses of camera202aare fabricated. Accordingly, the alignment system500determines the theoretical distortion function of the nominal lens system from the dimensions and properties of the materials specified for the nominal lenses (Step802). Alignment system500then computes the inverse of this distortion function (Step804), and select the desired shapes604and their pattern or layout (Step806). Alignment system500then generates the target shape510pattern by applying the selected pattern of shapes604to the inverse distortion function (Step808). In this manner, shapes510are pre-distorted versions of desired shapes604, distorted such that further distortion by the nominal lens system reverts them to desired shapes604.

In the foregoing description, aspects and embodiments of the present disclosure have been described with reference to numerous specific details that can vary from implementation to implementation. Accordingly, the description and drawings are to be regarded in an illustrative rather than a restrictive sense. The sole and exclusive indicator of the scope of the invention, and what is intended by the applicants to be the scope of the invention, is the literal and equivalent scope of the set of claims that issue from this application, in the specific form in which such claims issue, including any subsequent correction. Any definitions expressly set forth herein for terms contained in such claims shall govern the meaning of such terms as used in the claims. In addition, when we use the term “further comprising,” in the foregoing description or following claims, what follows this phrase can be an additional step or entity, or a sub-step/sub-entity of a previously-recited step or entity.