A positioning method, device, equipment, medium and product for a picking robot

Abstract

The present application discloses a positioning method, device, equipment, medium and product for a picking robot, which relates to the agricultural field. The method includes obtaining the pose of the Apriltag relative to the monocular camera at each moment according to the environmental images of the position where the picking robot is located acquired by the monocular camera at each moment; obtaining the relative poses of the picking robot at adjacent moments according to the environmental point cloud data of the position where the picking robot is located acquired by the lidar at each moment and the acceleration and angular velocity of the picking robot acquired by the IMU at each moment; constructing a factor graph; the nodes of the factor graph include the global poses of the picking robot and the Apriltag at each moment; the edges of the factor graph include: the pose of the Apriltag relative to the monocular camera at each moment and the relative poses of the picking robot at adjacent moments within the target time period; performing graph optimization on the factor graph to obtain the global pose of the picking robot at the current moment. The present application can improve the accuracy of the positioning result.

Description

Technical Field

[0001] The present application relates to the field of agriculture, and in particular, to a positioning method, device, equipment, medium and product for a picking robot. Background Art

[0002] With the development of agricultural intelligence, higher requirements are put forward for automated picking equipment. Especially in the farmland environment, picking robots need to have high-precision and stable positioning and navigation functions. However, in related technologies, a single vision or lidar positioning scheme is difficult to meet the complex requirements of the farmland environment. Vision positioning is easily affected by occlusion, while lidar positioning has low accuracy and cannot provide stable global pose information, resulting in inaccurate final positioning results for existing picking robot positioning methods. Summary of the Invention

[0003] The purpose of the present application is to provide a positioning method, device, equipment, medium and product for a picking robot, which can improve the accuracy of the positioning result.

[0004] To achieve the above purpose, the present application provides the following solutions.

[0005] In a first aspect, the present application provides a positioning method for a picking robot, including: obtaining the pose of the Apriltag relative to the monocular camera at each moment in a target time period according to the environmental images of the position where the picking robot is located obtained by the monocular camera at each moment in the target time period; the last moment of the target time period is the current moment.

[0006] Obtaining the relative pose of the picking robot at each adjacent moment in the target time period according to the environmental point cloud data of the position where the picking robot is located obtained by the lidar at each moment in the target time period and the acceleration and angular velocity of the picking robot obtained by the IMU at each moment in the target time period.

[0007] Constructing a factor graph; the nodes of the factor graph include the global pose of the picking robot and the global pose of the Apriltag at each moment in the target time period; the edges of the factor graph include: the pose of the Apriltag relative to the monocular camera at each moment in the target time period and the relative pose of the picking robot at each adjacent moment in the target time period.

[0008] Performing graph optimization on the factor graph to obtain the global pose of the picking robot at the current moment, and realizing the positioning of the picking robot.

[0009] In a second aspect, the present application provides a positioning device for a picking robot, including: a data processing module, configured to obtain the poses of Apriltags relative to the monocular camera at each moment within a target time period based on the environmental images of the position where the picking robot is located acquired by the monocular camera at each moment within the target time period; the last moment of the target time period is the current moment; and obtain the relative poses of the picking robot at adjacent moments within the target time period based on the environmental point cloud data of the position where the picking robot is located acquired by the lidar at each moment within the target time period and the acceleration and angular velocity of the picking robot acquired by the IMU at each moment within the target time period.

[0010] A graph optimization module, configured to construct a factor graph; the nodes of the factor graph include the global poses of the picking robot and the global poses of the Apriltags at each moment within the target time period; the edges of the factor graph include: the poses of the Apriltags relative to the monocular camera at each moment within the target time period and the relative poses of the picking robot at adjacent moments within the target time period; perform graph optimization on the factor graph to obtain the global pose of the picking robot at the current moment, thereby realizing the positioning of the picking robot.

[0011] In a third aspect, the present application provides a computer device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the picking robot positioning method described in any one of the above.

[0012] In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the picking robot positioning method described in any one of the above.

[0013] In a fifth aspect, the present application provides a computer program product, including a computer program, and when the computer program is executed by a processor, it implements the picking robot positioning method described in any one of the above.

[0014] According to the specific embodiments provided by the present application, the present application has the following technical effects: The present application provides a picking robot positioning method, device, equipment, medium and product. A single vision or lidar positioning scheme is difficult to meet the complex requirements of the farmland environment. Visual positioning is easily affected by occlusion, while lidar positioning has low accuracy and cannot provide stable global pose information, resulting in inaccurate final positioning results for existing picking robot positioning methods. The present application is based on multi-sensor data fusion, and by combining the use of the visual tag Apriltag, lidar and IMU, effectively solves the technical problem of unstable single-sensor positioning and improves the accuracy of the picking robot positioning result. Description of the Drawings

[0015] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the accompanying drawings required in the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can also be obtained based on these drawings.

[0016] Figure 1 It is a schematic flow chart of a positioning method for a picking robot provided by an embodiment of the present application.

[0017] Figure 2 It is a schematic diagram of the functional modules of a positioning device for a picking robot provided by an embodiment of the present application.

[0018] Figure 3 It is a schematic diagram of the structure of a computer device provided by an embodiment of the present application. Detailed implementation manners

[0019] The following will clearly and completely describe the technical solutions in the embodiments of the present application in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, rather than all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0020] To make the above objects, features, and advantages of the present application more obvious and understandable, the present application will be further described in detail below in conjunction with the accompanying drawings and specific implementation manners.

[0021] In an exemplary embodiment, as Figure 1 shown, a positioning method for a picking robot is provided, including the following steps.

[0022] Step 201: Obtain the pose of the Apriltag relative to the monocular camera at each moment in the target time period according to the environmental images of the position of the picking robot obtained by the monocular camera at each moment in the target time period; the last moment of the target time period is the current moment.

[0023] Step 202: Obtain the relative poses of the picking robot at adjacent moments in the target time period according to the environmental point cloud data of the position of the picking robot obtained by the lidar at each moment in the target time period and the acceleration and angular velocity of the picking robot obtained by the IMU (Inertial Measurement Unit) at each moment in the target time period.

[0024] Step 203: Construct a factor graph; the nodes of the factor graph include the global poses of the picking robot at each moment within the target time period (to be solved) and the global poses of the Apriltags (representing the fixed poses of the Apriltags in the global coordinate system, which can be measured by external tools such as laser rangefinders during environmental layout. The poses of the Apriltags in the environment are fixed, and their global poses do not change over time, so they are known prior data); the edges of the factor graph include: the poses of the Apriltags relative to the monocular camera at each moment within the target time period and the relative poses of the picking robot at each adjacent moment within the target time period. In graph optimization, nodes can be considered as variables, so in this application, the global poses of the robot and the global poses of the Apriltags are used as nodes in the factor graph.

[0025] Step 204: Perform graph optimization on the factor graph to obtain the global pose of the picking robot at the current moment, thereby realizing the positioning of the picking robot. Through graph optimization calculation, the pose information of the robot relative to the global coordinate system is obtained for further navigation and path planning.

[0026] Implementing the above Steps 201 to 204 can improve the accuracy of the positioning result.

[0027] In another exemplary embodiment of this application, the execution entity of this application is the robot system controller jetsontx2.

[0028] In another exemplary embodiment of this application, the poses of the Apriltags relative to the monocular camera at each moment within the target time period are obtained according to the environmental images of the position where the picking robot is located acquired by the monocular camera at each moment within the target time period. Specifically, for the t-th moment within the target time period, the Apriltag algorithm is used to process the environmental image of the position where the picking robot is located acquired by the monocular camera at the t-th moment, and the pose of the Apriltag relative to the monocular camera at the t-th moment is obtained.

[0029] In another exemplary embodiment of the present application, the relative poses of the picking robot at adjacent moments within the target time period are obtained based on the environmental point cloud data of the position of the picking robot acquired by the lidar at each moment within the target time period and the acceleration and angular velocity of the picking robot acquired by the IMU at each moment within the target time period. Specifically, for the t-th moment within the target time period, the FastLIO2 (Fast LiDAR-Inertial Odometry 2) algorithm is used to process the environmental point cloud data of the position of the picking robot acquired by the lidar at the t-th moment and the acceleration and angular velocity of the picking robot acquired by the IMU at the t-th moment, so as to obtain the relative pose of the picking robot at the t-th moment and the (t - 1)-th moment. FastLIO2 is an open-source lidar-inertial odometry (LIO) algorithm, which has the characteristics of real-time, high-precision, and lightweight.

[0030] In another exemplary embodiment of the present application, graph optimization is performed on the factor graph to obtain the global pose of the picking robot at the current moment. Specifically, the ISAM2 algorithm is used to perform graph optimization on the factor graph to obtain the global pose of the picking robot at the current moment.

[0031] Graph optimization is a non-linear optimization method based on factor graphs, which is widely used in robot positioning and navigation. By modeling pose information and observation information as nodes and edges in a graph, and using an optimization algorithm to minimize the error of the edges, a globally consistent pose result is finally optimized. In the factor graph, the edges are used to describe the constraint relationships between two nodes. Therefore, in the present application, two constraint relationships are established between the unknown node of the global pose of the robot and the known node of the global pose of the Apriltag. Establish two constraint relationships.

[0032] One is the odometry pose edge, which is represented by the constraint equation where, represents the function that converts the Lie group to the six-dimensional vector corresponding to the Lie algebra , represents the calculation of transpose, represents Lie group of converted to the six-dimensional vector corresponding to the Lie algebra , represents the calculation of inverse matrix, represents the calculation of covariance matrix, represents the i-th moment odometry pose edge, represents the calculation of The inverse matrix of is the global pose of the picking robot at the i-th moment, is the global pose of the picking robot at the (i - 1)-th moment, representing variables at two time nodes, is the odometry pose, i.e., the algorithm output of Fastlio2, representing the relative pose of the picking robot between the (i - 1)-th moment and the i-th moment.

[0033] Another one is the Apriltag relative robot pose edge, which is represented by the constraint equation where, represents the Apriltag relative robot pose edge at the i-th moment, represents the calculation of the transpose of represents the conversion of the Lie group of to the Lie algebra the function corresponding to the six-dimensional vector, represents the calculation of the inverse matrix of represents the calculation of the covariance matrix of represents the global pose of the Apriltag, represents the calculation of the inverse matrix of represents the pose of the Apriltag relative to the picking robot coordinate system at the i-th moment, , is the pose of the Apriltag relative to the monocular camera at the i-th moment, represents the pose from the camera to the picking robot coordinate system, which is the external parameter of the monocular camera and does not change with time. The specific parameters are determined by the mechanical structure before the algorithm runs. The odometry pose edge provides the motion constraint between consecutive moments of the robot, ensuring the continuity and real-time update of the trajectory; the Apriltag relative robot pose edge provides the relative pose constraint between the robot and the global reference Apriltag, which helps to correct the drift of the odometry estimation. Pose optimization: Establish a non-linear least squares objective function to optimize the global pose of the picking robot at all moments: where, represents the global pose of the picking robot at each moment within the target time period, which is the set of the global poses of the picking robot from the initial moment to the current moment k , Since this optimization needs to be carried out in real time and increases continuously over time, the number of edges to be optimized increases continuously, i.e., k increases continuously. Ordinary optimization algorithms are difficult to optimize in real time. Since the ISAM2 algorithm can achieve real-time graph optimization based on the incremental update characteristics of the Bayesian tree, the ISAM2 algorithm in the GTSAM library is selected for real-time graph optimization calculation. The input of the ISAM2 algorithm is the nodes and edges of the graph, which optimizes the above non-linear least squares problem, and the output is the global pose of the picking robot at all times. , including the global pose of the picking robot at the current time. . That is, in another exemplary embodiment of the present application, the ISAM2 algorithm is used to perform graph optimization on the factor graph to obtain the global pose of the picking robot at the current time. Specifically: the ISAM2 algorithm is used to perform optimization and solution to obtain the global pose of the picking robot at the current time, where k represents the current time.

[0034] In another exemplary embodiment of the present application, the monocular camera and the lidar are both arranged on the picking robot, and the IMU is built in the lidar. Specifically, the lidar is the MID360 lidar, which obtains the point cloud data of the surrounding environment, and the MID360 lidar built in the IMU obtains its own motion information (the acceleration and angular velocity ) of the picking robot.

[0035] The present application combines the environmental information obtained by the monocular camera and the lidar (built-in IMU), and uses the global positioning of the visual tag Apriltag and the relative positioning of the FastLIO2 odometer fused by the lidar and the IMU to achieve real-time high-precision global positioning of the robot in the agricultural environment. This method is mainly applied to the navigation and positioning system of automated picking equipment.

[0036] Compared with the existing single-sensor positioning methods, the present application has achieved the following advantages through the fusion positioning method of vision and lidar sensors.

[0037] 1. High-precision positioning: By using the method of solving the pose by visual recognition of Apriltag and the combination of lidar + IMU, the robot can maintain a high positioning accuracy in the complex farmland environment.

[0038] 2. Strong real-time performance: Using the ISAM2 graph optimization algorithm for real-time calculation, the robot can instantaneously update its global pose during the navigation process.

[0039] 3. Good stability: Even when part of the field of vision is easily blocked in the farmland, the position of the robot can still be calculated through the point cloud data of the lidar and the self-motion data of the IMU (the acceleration and angular velocity of the picking robot), making the overall positioning solution have stronger environmental adaptability.

[0040] Based on the same inventive concept, the embodiments of the present application also provide a picking robot positioning device for implementing the above-mentioned picking robot positioning method. The solution provided by this device to solve the problem is similar to the solution described in the above method. Therefore, the specific limitations in one or more of the following embodiments of the picking robot positioning device can refer to the limitations on the picking robot positioning method in the above text, and will not be repeated here.

[0041] In an exemplary embodiment, as Figure 2 shown, a picking robot positioning device is provided, including: a data processing module, configured to obtain the pose of the Apriltag relative to the monocular camera at each moment within the target time period according to the environmental images of the position of the picking robot acquired by the monocular camera at each moment within the target time period; the last moment of the target time period is the current moment; according to the environmental point cloud data of the position of the picking robot acquired by the lidar at each moment within the target time period and the acceleration and angular velocity of the picking robot acquired by the IMU at each moment within the target time period, obtain the relative pose of the picking robot at each adjacent moment within the target time period.

[0042] A graph optimization module, configured to construct a factor graph; the nodes of the factor graph include the global poses of the picking robot and the Apriltag at each moment within the target time period; the edges of the factor graph include: the pose of the Apriltag relative to the monocular camera at each moment within the target time period and the relative pose of the picking robot at each adjacent moment within the target time period; perform graph optimization on the factor graph to obtain the global pose of the picking robot at the current moment, and realize the positioning of the picking robot.

[0043] In an exemplary embodiment, the picking robot positioning device further includes: a data acquisition module, and the data acquisition module includes a monocular camera, an MID360 lidar, and a built-in IMU.

[0044] In an exemplary embodiment, the data processing module: includes an Apriltag recognition and pose solution module and a FastLIO2 odometry calculation method module.

[0045] The Apriltag recognition and pose solution module is configured to obtain the pose of the Apriltag relative to the monocular camera at each moment within the target time period according to the environmental images of the position of the picking robot acquired by the monocular camera at each moment within the target time period.

[0046] The FastLIO2 odometry calculation module is used to obtain the relative poses of the picking robot at adjacent moments within the target time period based on the environmental point cloud data of the position of the picking robot obtained by the lidar at each moment within the target time period and the acceleration and angular velocity of the picking robot obtained by the IMU at each moment within the target time period.

[0047] In an exemplary embodiment, the graph optimization module: performs graph optimization on the factor graph based on the ISAM2 algorithm in the GTSAM library to obtain the global pose of the picking robot at the current moment.

[0048] In an exemplary embodiment, a computer device is provided. The computer device can be a server or a terminal, and its internal structure diagram can be as Figure 3 shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the positioning data of the picking robot. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it implements a picking robot positioning method.

[0049] Those skilled in the art can understand that Figure 3 the structure shown in

[0050] is only a block diagram of some structures related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements. In an exemplary embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the above method embodiments are implemented.

[0051] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by the processor, the above method embodiments are implemented.

[0052] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data that have been authorized by the user or fully authorized by all parties, and the collection, use, and processing of the relevant data need to comply with the relevant regulations.

[0053] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the various embodiments provided in this application can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0054] The databases involved in the various embodiments provided in this application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the various embodiments provided in this application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., without limitation.

[0055] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0056] Specific examples are used in this article to elaborate on the principles and implementation manners of this application. The description of the above embodiments is only used to help understand the method and its core idea of this application; at the same time, for those of ordinary skill in the art, according to the idea of this application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to this application.

Claims

1. A picking robot positioning method, characterized in that: The picking robot positioning method comprises: The position and posture of Apriltag relative to the monocular camera at each moment in the target time period are obtained according to the environmental image of the picking robot's position obtained by the monocular camera at each moment in the target time period; the last moment in the target time period is the current moment; The relative position and posture of the picking robot at each adjacent moment in the target time period are obtained based on the environmental point cloud data of the picking robot's position obtained by the laser radar at each moment in the target time period and the acceleration and angular velocity of the picking robot obtained by the IMU at each moment in the target time period; Construct a factor graph; the nodes of the factor graph include the global pose of the picking robot and the global pose of Apriltag at each moment in the target time period; the edges of the factor graph include: the pose of Apriltag relative to the monocular camera at each moment in the target time period and the relative pose of the picking robot at each adjacent moment in the target time period; Optimizing the factor graph to obtain the global pose of the picking robot at the current moment, thereby realizing the positioning of the picking robot; The factor graph is optimized to obtain the global posture of the picking robot at the current moment. Specifically, the ISAM2 algorithm is used to optimize the factor graph. The global position of the picking robot at the current moment is obtained by optimization, where: represents the global position of the picking robot at each moment in the target time period, k represents the current moment, Representation calculation The transpose of Indicates that Lie Group Convert to Lie algebra The corresponding six-dimensional vector function is represents the global pose of Apriltag, Representation calculation The inverse matrix of represents the global pose of the picking robot at the i-th moment, represents the position of Apriltag relative to the picking robot coordinate system at the i-th moment, , represents the external parameters of the monocular camera, represents the position of Apriltag relative to the monocular camera at the i-th moment, Representation calculation The inverse matrix of represents the global position of the picking robot at the i-1th moment, represents the relative position of the picking robot at the i-th moment and the i-1-th moment, Representation calculation The inverse matrix of Representation calculation The covariance matrix of Representation calculation The transpose of Indicates that Lie Group Convert to Lie algebra The corresponding six-dimensional vector function is Representation calculation The inverse matrix of Representation calculation The covariance matrix of .

2. The picking robot positioning method according to claim 1, characterized in that: According to the environmental image of the picking robot's location acquired by the monocular camera at each moment in the target time period, the position and posture of Apriltag relative to the monocular camera at each moment in the target time period are obtained, specifically including: For the tth moment in the target time period, the Apriltag algorithm is used to process the environmental image of the picking robot's location obtained by the monocular camera at the tth moment to obtain the position and pose of Apriltag relative to the monocular camera at the tth moment.

3. The picking robot positioning method according to claim 1, characterized in that: The relative position and posture of the picking robot at each adjacent moment in the target time period are obtained based on the environmental point cloud data of the picking robot's position obtained by the lidar at each moment in the target time period and the acceleration and angular velocity of the picking robot obtained by the IMU at each moment in the target time period, including: For the tth moment in the target time period, the FastLIO2 algorithm is used to process the environmental point cloud data of the picking robot's position obtained by the lidar at the tth moment and the acceleration and angular velocity of the picking robot obtained by the IMU at the tth moment to obtain the relative pose of the picking robot at the tth moment and the t-1th moment.

4. The picking robot positioning method according to claim 1, characterized in that: The monocular camera and the laser radar are both arranged on the picking robot, and the laser radar has the IMU built in.

5. A picking robot positioning device, characterized in that: The picking robot positioning device comprises: The data processing module is used to obtain the position and posture of Apriltag relative to the monocular camera at each moment in the target time period according to the environmental image of the picking robot's position obtained by the monocular camera at each moment in the target time period; the last moment in the target time period is the current moment; the relative position and posture of the picking robot at each adjacent moment in the target time period is obtained according to the environmental point cloud data of the picking robot's position obtained by the lidar at each moment in the target time period and the acceleration and angular velocity of the picking robot obtained by the IMU at each moment in the target time period; A graph optimization module is used to construct a factor graph; the nodes of the factor graph include the global pose of the picking robot at each moment in the target time period and the global pose of Apriltag; the edges of the factor graph include: the pose of Apriltag relative to the monocular camera at each moment in the target time period and the relative pose of each adjacent moment in the target time period of the picking robot; the factor graph is optimized to obtain the global pose of the picking robot at the current moment, so as to realize the positioning of the picking robot; the factor graph is optimized to obtain the global pose of the picking robot at the current moment, specifically: the ISAM2 algorithm is used to optimize The global position of the picking robot at the current moment is obtained by optimization, where: represents the global position of the picking robot at each moment in the target time period, k represents the current moment, Representation calculation The transpose of Indicates that Lie Group Convert to Lie algebra The corresponding six-dimensional vector function is represents the global pose of Apriltag, Representation calculation The inverse matrix of represents the global pose of the picking robot at the i-th moment, represents the position of Apriltag relative to the picking robot coordinate system at the i-th moment, , represents the external parameters of the monocular camera, represents the position of Apriltag relative to the monocular camera at the i-th moment, Representation calculation The inverse matrix of represents the global position of the picking robot at the i-1th moment, represents the relative position of the picking robot at the i-th moment and the i-1-th moment, Representation calculation The inverse matrix of Representation calculation The covariance matrix of Representation calculation The transpose of Indicates that Lie Group Convert to Lie algebra The corresponding six-dimensional vector function is Representation calculation The inverse matrix of Representation calculation The covariance matrix of .

6. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the picking robot positioning method according to any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the picking robot positioning method described in any one of claims 1 to 4 is implemented.

8. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the picking robot positioning method described in any one of claims 1 to 4 is implemented.

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