Joint calibration method, device and equipment of lidar and camera, and medium

Abstract

This invention discloses a joint calibration method, apparatus, device, and medium for a lidar and a camera. The method includes: acquiring two-dimensional image information obtained by the camera detecting a target, and acquiring three-dimensional point cloud information obtained by the lidar detecting the target; determining first position information of a first key point in the two-dimensional image information, and determining second position information of a second key point in the three-dimensional point cloud information; wherein the relative positional relationship between the first key point and a target hole is the same as the relative positional relationship between the second key point and a target hole; determining a homography matrix based on the first and second position information, and calibrating the relative positional relationship between the camera and the lidar based on the homography matrix. This technical solution calibrates the camera and lidar based on the key point positions in the two-dimensional image information and the three-dimensional point cloud information, improving calibration efficiency and accuracy, and exhibiting strong robustness and flexibility.

Description

Technical Field

[0001] This invention relates to the field of intelligent driving technology, and in particular to a method, apparatus, device and medium for joint calibration of lidar and camera. Background Technology

[0002] In intelligent driving systems, the limitations of a single sensor are unavoidable. To further improve the robustness of the system, target detection is achieved by fusing perception results from multiple sensors. Among these, the accuracy of the joint calibration results from LiDAR and cameras directly affects the precision of perception fusion and joint annotation in the intelligent driving backend.

[0003] Currently, most joint calibration methods for lidar and cameras are manual, but this method has low calibration efficiency and accuracy, and poor flexibility, making it difficult to meet practical needs. Summary of the Invention

[0004] This invention provides a method, apparatus, device, and medium for joint calibration of a lidar and a camera. It performs joint calibration of the camera and lidar based on the key point positions in two-dimensional image information and three-dimensional point cloud information, which improves calibration efficiency and accuracy and has strong robustness and flexibility.

[0005] According to one aspect of the present invention, a joint calibration method for a lidar and a camera is provided, the method comprising:

[0006] The system acquires two-dimensional image information obtained by a camera detecting a target, and acquires three-dimensional point cloud information obtained by a lidar detecting the target; wherein the target has at least one target hole.

[0007] The first position information of the first key point in the two-dimensional image information is determined, and the second position information of the second key point in the three-dimensional point cloud information is determined; wherein, the relative positional relationship between the first key point and the target hole and the relative positional relationship between the second key point and the target hole are the same;

[0008] A homography matrix is ​​determined based on the first location information and the second location information, and the relative positional relationship between the camera and the lidar is calibrated based on the homography matrix.

[0009] According to another aspect of the present invention, a joint calibration device for a lidar and a camera is provided, comprising:

[0010] The target detection information acquisition module is used to acquire two-dimensional image information obtained by the camera detecting the target, and to acquire three-dimensional point cloud information obtained by the lidar detecting the target; wherein, the target has at least one target hole.

[0011] The key point location information determination module is used to determine the first location information of the first key point in the two-dimensional image information and the second location information of the second key point in the three-dimensional point cloud information; wherein, the relative positional relationship between the first key point and the target hole and the relative positional relationship between the second key point and the target hole are the same;

[0012] The relative positional relationship calibration module is used to determine a homography matrix based on the first position information and the second position information, and to calibrate the relative positional relationship between the camera and the lidar based on the homography matrix.

[0013] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0014] At least one processor; and

[0015] A memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the joint calibration method of lidar and camera according to any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the joint calibration method of lidar and camera as described in any embodiment of the present invention.

[0018] The technical solution of this invention involves acquiring two-dimensional image information obtained by a camera detecting a target, and acquiring three-dimensional point cloud information obtained by a lidar detecting the target. The target has at least one hole. The solution determines the first position information of a first key point in the two-dimensional image information and the second position information of a second key point in the three-dimensional point cloud information. The relative positional relationship between the first key point and the target hole is the same as that between the second key point and the target hole. A homography matrix is ​​determined based on the first and second position information, and the relative positional relationship between the camera and the lidar is calibrated based on the homography matrix. This technical solution, by jointly calibrating the camera and lidar based on the key point positions in the two-dimensional image information and the three-dimensional point cloud information, improves calibration efficiency and accuracy, and has strong robustness and flexibility.

[0019] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart of a joint calibration method for a lidar and a camera according to Embodiment 1 of the present invention;

[0022] Figure 2 This is a flowchart of a joint calibration method for a lidar and a camera according to Embodiment 2 of the present invention;

[0023] Figure 3 This is a schematic diagram of the structure of a joint calibration device for a lidar and a camera according to Embodiment 3 of the present invention;

[0024] Figure 4 This is a schematic diagram of the structure of an electronic device that implements a joint calibration method for a lidar and a camera according to an embodiment of the present invention. Detailed Implementation

[0025] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0026] It should be noted that the terms "first," "second," "target," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0027] Example 1

[0028] Figure 1 This is a flowchart of a joint calibration method for a lidar and a camera provided in Embodiment 1 of the present invention. This embodiment is applicable to situations requiring efficient and accurate calibration of lidar and cameras. The method can be executed by a joint calibration device for lidar and cameras, which can be implemented in hardware and / or software. This joint calibration device can be configured in an electronic device with data processing capabilities. Figure 1 As shown, the method includes:

[0029] S110, acquire two-dimensional image information obtained by the camera detecting the target, and acquire three-dimensional point cloud information obtained by the lidar detecting the target; wherein, the target has at least one target hole.

[0030] The target can be a pre-defined calibration reference object with at least one target hole. Optionally, the target is a rectangular calibration plate, and the target hole is circular. It should be noted that the number and location of the target holes can be set according to actual needs, and this embodiment does not impose specific limitations on this. The camera and lidar are pre-installed on the target vehicle. Optionally, the camera is a wide-angle camera (e.g., with a 120-degree field of view), and the lidar is a solid-state lidar. Alternatively, the camera can be a narrow-angle camera, and the lidar can be a mechanical lidar.

[0031] In this embodiment, firstly, two-dimensional image information (i.e., RGB image information) is acquired by capturing the target with a camera, and then three-dimensional point cloud information is acquired by sensing the target with a LiDAR. It should be noted that, since the target occupies a small area within the 120-degree field of view of the forward-looking camera, and full-image detection takes too long, to save computational power, a region of interest including the target can be cropped from the original image information captured by the camera, based on empirical values, as the two-dimensional image information. Similarly, since the target occupies a small area within the field of view of the solid-state LiDAR, and full point cloud detection takes too long, to save computational power, a region of interest including the target can be cropped from the original point cloud information sensed by the LiDAR, based on empirical values, as the three-dimensional point cloud information.

[0032] S120, determine the first position information of the first key point in the two-dimensional image information, and determine the second position information of the second key point in the three-dimensional point cloud information; wherein, the relative positional relationship between the first key point and the target hole is the same as the relative positional relationship between the second key point and the target hole.

[0033] Here, the first key point can refer to a detection point in the two-dimensional image information. The first position information can refer to the position information of the first key point. The second key point can refer to a detection point in the three-dimensional point cloud information. The second position information can refer to the position information of the second key point. It should be noted that the relative positional relationship between the first key point and the target hole is the same as that between the second key point and the target hole. For example, the center point of the target hole in the two-dimensional image information can be used as the first key point, and the center point of the target hole in the three-dimensional point cloud information can be used as the second key point.

[0034] In this embodiment, for example, after acquiring the two-dimensional image information, the target in the two-dimensional image information is first identified, then all target holes on the target are identified, and then the center point coordinates (two-dimensional coordinates) of the target holes are determined as the first position information of the first key point based on the established two-dimensional image coordinate system. Similarly, after acquiring the three-dimensional point cloud information, the target in the three-dimensional point cloud information is first identified, then all target holes on the target are identified, and then the center point coordinates (three-dimensional coordinates) of the target holes are determined as the second position information of the second key point based on the established three-dimensional point cloud coordinate system. It should be noted that the identification method for the target and target holes is not specifically limited and can be set according to actual needs. For example, identification can be based on an edge detection algorithm.

[0035] S130, determine the homography matrix based on the first position information and the second position information, and calibrate the relative positional relationship between the camera and the lidar based on the homography matrix.

[0036] In this embodiment, after determining the first and second position information, a homography matrix can be determined based on the first and second position information. This allows the second position information to be converted into two-dimensional image information, and then the relative positional relationship between the camera and the LiDAR can be calibrated based on the homography matrix. The homography matrix can be a projection matrix from one plane to another, used to describe the positional mapping relationship of an object between different planes. Furthermore, the calculation method of the homography matrix can be found in existing technologies and will not be repeated in this embodiment. The homography matrix can describe the relative positional relationship (including angle and distance) between the camera and the LiDAR, thereby enabling joint calibration of the camera and the LiDAR.

[0037] Furthermore, to improve the calibration accuracy, after determining the homography matrix, the position of the target relative to the camera and lidar can be changed, and steps S110-S130 can be re-executed according to the updated target position. The initially calculated homography matrix can then be optimized based on the updated homography matrix.

[0038] The technical solution of this invention involves acquiring two-dimensional image information obtained by a camera detecting a target, and acquiring three-dimensional point cloud information obtained by a lidar detecting the target. The target has at least one hole. The solution determines the first position information of a first key point in the two-dimensional image information and the second position information of a second key point in the three-dimensional point cloud information. The relative positional relationship between the first key point and the target hole is the same as that between the second key point and the target hole. A homography matrix is ​​determined based on the first and second position information, and the relative positional relationship between the camera and the lidar is calibrated based on the homography matrix. This technical solution, by jointly calibrating the camera and lidar based on the key point positions in the two-dimensional image information and the three-dimensional point cloud information, improves calibration efficiency and accuracy, and has strong robustness and flexibility.

[0039] In this embodiment, optionally, a target marker is also provided on the target, which is used to mark the outline information of the target.

[0040] The target marker can be used to calibrate the contour information of the target. For example, the target marker can be a QR code. It should be noted that when using edge detection algorithms to identify the target, there may be cases where recognition fails, thus affecting the accurate calibration of the camera and LiDAR. Therefore, pre-set target markers on the target can be used to identify the target, thereby improving the accuracy of target identification.

[0041] For example, when the target is a rectangular calibration plate, a QR code can be placed at each of the four vertices of the rectangular calibration plate to calibrate its contour information. During target identification, the four vertices of the rectangular calibration plate can be located by identifying the four QR codes. Connecting the four vertices sequentially yields the contour information of the rectangular calibration plate, thereby achieving accurate and effective target identification.

[0042] This solution, through this setup, can identify the target by using pre-set target markers on the target, thereby improving the accuracy of target identification.

[0043] Example 2

[0044] Figure 2 This is a flowchart of a joint calibration method for a lidar and a camera provided in Embodiment 2 of the present invention. This embodiment is an optimization based on the above embodiment. Specifically, the optimization includes: after calibrating the relative positional relationship between the camera and the lidar according to the homography matrix, the method further includes: projecting all point clouds in the three-dimensional point cloud information onto the two-dimensional image information according to the homography matrix; determining the number of invalid point clouds in the three-dimensional point cloud information that do not overlap with the two-dimensional image information according to the projection result; determining the projection error according to the number of invalid point clouds and the total number of point clouds in the three-dimensional point cloud information; and determining the calibration result according to the projection error.

[0045] like Figure 2 As shown, the method in this embodiment specifically includes the following steps:

[0046] S210, acquire two-dimensional image information obtained by the camera detecting the target, and acquire three-dimensional point cloud information obtained by the lidar detecting the target; wherein, the target has at least one target hole.

[0047] S220, determine the first position information of the first key point in the two-dimensional image information, and determine the second position information of the second key point in the three-dimensional point cloud information; wherein, the relative positional relationship between the first key point and the target hole is the same as the relative positional relationship between the second key point and the target hole.

[0048] S230, determine the homography matrix based on the first position information and the second position information, and calibrate the relative positional relationship between the camera and the lidar based on the homography matrix.

[0049] The specific implementation methods of S210-S230 can be found in the detailed description of S110-S130, and will not be repeated here.

[0050] S240 projects all point cloud information from the three-dimensional point cloud information onto the two-dimensional image information based on the homography matrix.

[0051] In this embodiment, after calibrating the relative positional relationship between the camera and the lidar based on the homography matrix, all point clouds in the 3D point cloud information can be projected onto the 2D image information to verify the accuracy of the calibration. Specifically, the positional information of each point cloud in the 3D point cloud information is obtained, and the projection result of each point cloud can be determined based on the positional information of each point cloud and the homography matrix. The projection result includes whether the point cloud overlaps with the 2D image information or not.

[0052] S250, determine the number of invalid point clouds in the three-dimensional point cloud information that do not overlap with the two-dimensional image information based on the projection results.

[0053] The number of invalid point clouds can refer to the sum of the number of point clouds in the 3D point cloud information that do not overlap with the 2D image information. In this embodiment, when determining the number of invalid point clouds based on the projection results, the number of point clouds in the 3D point cloud information that do not overlap with the 2D image information can be directly accumulated to obtain the number of invalid point clouds. If the projected point cloud appears in the foreground area of ​​the 2D image, it can be determined that the point cloud overlaps with the 2D image information; if the projected point cloud appears in the background area of ​​the 2D image, it can be determined that the point cloud does not overlap with the 2D image information.

[0054] S260, determine the projection error based on the number of invalid point clouds and the total number of point clouds in the 3D point cloud information, and determine the calibration result based on the projection error.

[0055] In this embodiment, after determining the number of invalid point clouds in the 3D point cloud information, the projection error can be determined based on the ratio of the number of invalid point clouds to the total number of point clouds in the 3D point cloud information, and then the calibration result can be determined based on the projection error. Optionally, determining the calibration result based on the projection error includes: if the projection error is greater than a preset error value, then the calibration result is determined to be a calibration failure; otherwise, the calibration result is determined to be a calibration success.

[0056] The preset error value refers to a pre-set projection error reference value, which can be set according to actual needs. Specifically, if the projection error is greater than the preset error value, it indicates that the projection error is large (i.e., the calibration accuracy is low), and the calibration result can be determined as calibration failure; if the projection error is less than or equal to the preset error value, it indicates that the projection error is small (i.e., the calibration accuracy is high), and the calibration result can be determined as calibration success.

[0057] This solution, through such settings, can quickly and accurately verify the calibration accuracy based on the relationship between the projection error and the preset error value.

[0058] The technical solution of this invention, after calibrating the relative positional relationship between the camera and the lidar based on the homography matrix, projects all point clouds from the 3D point cloud information onto the 2D image information based on the homography matrix; determines the number of invalid point clouds in the 3D point cloud information that do not overlap with the 2D image information based on the projection result; determines the projection error based on the number of invalid point clouds and the total number of point clouds in the 3D point cloud information; and determines the calibration result based on the projection error. This technical solution, by jointly calibrating the camera and lidar based on the key point positions in the 2D image information and 3D point cloud information, improves calibration efficiency and accuracy, has strong robustness and flexibility, and can also quickly and accurately verify the calibration accuracy based on the projection error.

[0059] Example 3

[0060] Figure 3 This is a schematic diagram of a joint calibration device for a lidar and a camera provided in Embodiment 3 of the present invention. This device can execute the joint calibration method for a lidar and a camera provided in any embodiment of the present invention, and possesses the corresponding functional modules and beneficial effects for executing the method. For example... Figure 3 As shown, the device includes:

[0061] The target detection information acquisition module 310 is used to acquire two-dimensional image information obtained by the camera detecting the target, and to acquire three-dimensional point cloud information obtained by the lidar detecting the target; wherein, the target is provided with at least one target hole;

[0062] The key point location information determination module 320 is used to determine the first location information of the first key point in the two-dimensional image information and the second location information of the second key point in the three-dimensional point cloud information; wherein, the relative positional relationship between the first key point and the target hole and the relative positional relationship between the second key point and the target hole are the same;

[0063] The relative position relationship calibration module 330 is used to determine a homography matrix based on the first position information and the second position information, and to calibrate the relative position relationship between the camera and the lidar based on the homography matrix.

[0064] Optionally, the device further includes:

[0065] The point cloud projection module is used to project all the point clouds in the three-dimensional point cloud information into the two-dimensional image information according to the homography matrix after calibrating the relative positional relationship between the camera and the lidar according to the homography matrix.

[0066] The invalid point cloud quantity determination module is used to determine the number of invalid point clouds in the three-dimensional point cloud information that do not overlap with the two-dimensional image information based on the projection results;

[0067] The calibration result determination module is used to determine the projection error based on the number of invalid point clouds and the total number of point clouds in the three-dimensional point cloud information, and to determine the calibration result based on the projection error.

[0068] Optionally, the calibration result determination module is used for:

[0069] If the projection error is greater than the preset error value, the calibration result is determined to be a calibration failure;

[0070] Otherwise, the calibration result is considered a successful calibration.

[0071] Optionally, the target may also be provided with a target marker, which is used to mark the outline information of the target.

[0072] Optionally, the target is a rectangular calibration plate, and the target hole is a circular hole.

[0073] Optionally, the camera is a wide-angle camera, and the lidar is a solid-state lidar.

[0074] The joint calibration device for lidar and camera provided in this embodiment of the invention can execute the joint calibration method for lidar and camera provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0075] Example 4

[0076] Figure 4 A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0077] like Figure 4As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0078] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0079] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the joint calibration method of LiDAR and camera.

[0080] In some embodiments, the joint calibration method for LiDAR and camera can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the joint calibration method for LiDAR and camera described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the joint calibration method for LiDAR and camera by any other suitable means (e.g., by means of firmware).

[0081] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0082] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0083] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0084] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0085] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0086] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0087] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0088] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A joint calibration method for lidar and camera, characterized in that, The method includes: The system acquires two-dimensional image information obtained by a camera detecting a target, and acquires three-dimensional point cloud information obtained by a lidar detecting the target; wherein the target has at least one target hole. The first position information of the first key point in the two-dimensional image information is determined, and the second position information of the second key point in the three-dimensional point cloud information is determined; wherein, the relative positional relationship between the first key point and the target hole and the relative positional relationship between the second key point and the target hole are the same; A homography matrix is ​​determined based on the first position information and the second position information, and the relative positional relationship between the camera and the lidar is calibrated based on the homography matrix; wherein, the homography matrix is ​​a projection matrix from the first position information to the second position information; Based on the homography matrix, all point clouds in the three-dimensional point cloud information are projected onto the two-dimensional image information; The number of invalid point clouds that do not overlap with the two-dimensional image information in the three-dimensional point cloud information is determined based on the projection results. The projection error is determined based on the number of invalid point clouds and the total number of point clouds in the 3D point cloud information. If the projection error is greater than a preset error value, the calibration result is determined to be a calibration failure; otherwise, the calibration result is determined to be a calibration success.

2. The method according to claim 1, characterized in that, The target is also provided with a target marker, which is used to mark the outline information of the target.

3. The method according to claim 1, characterized in that, The target is a rectangular calibration plate, and the target hole is a circular hole.

4. The method according to claim 1, characterized in that, The camera is a wide-angle camera, and the lidar is a solid-state lidar.

5. A joint calibration device for lidar and camera, characterized in that, The device includes: The target detection information acquisition module is used to acquire two-dimensional image information obtained by the camera detecting the target, and to acquire three-dimensional point cloud information obtained by the lidar detecting the target; wherein, the target has at least one target hole. The key point location information determination module is used to determine the first location information of the first key point in the two-dimensional image information and the second location information of the second key point in the three-dimensional point cloud information; wherein, the relative positional relationship between the first key point and the target hole and the relative positional relationship between the second key point and the target hole are the same; A relative positional relationship calibration module is used to determine a homography matrix based on the first position information and the second position information, and to calibrate the relative positional relationship between the camera and the lidar based on the homography matrix; wherein, the homography matrix is ​​a projection matrix from the first position information to the second position information; The point cloud projection module is used to project all the point clouds in the three-dimensional point cloud information into the two-dimensional image information according to the homography matrix after calibrating the relative positional relationship between the camera and the lidar according to the homography matrix. The invalid point cloud quantity determination module is used to determine the number of invalid point clouds in the three-dimensional point cloud information that do not overlap with the two-dimensional image information based on the projection results; The calibration result determination module is used to determine the projection error based on the number of invalid point clouds and the total number of point clouds in the three-dimensional point cloud information. If the projection error is greater than a preset error value, the calibration result is determined to be a calibration failure; otherwise, the calibration result is determined to be a calibration success.

6. The apparatus according to claim 5, characterized in that, The target is also provided with a target marker, which is used to mark the outline information of the target.

7. The apparatus according to claim 5, characterized in that, The target is a rectangular calibration plate, and the target hole is a circular hole.

8. The apparatus according to claim 5, characterized in that, The camera is a wide-angle camera, and the lidar is a solid-state lidar.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the joint calibration method of lidar and camera as described in any one of claims 1-4.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the joint calibration method of the lidar and camera as described in any one of claims 1-4.

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