Mobile V2G robot positioning method and device based on fusion of laser radar and inertial measurement unit, and medium

Abstract

The invention provides a mobile V2G robot positioning method and device based on fusion of a laser radar and an inertial measurement unit, and a medium, and belongs to the technical field of mobile charging robot positioning. The method comprises the following steps: a robot scans the surrounding environment through a laser radar to obtain point cloud data, and an inertial measurement unit measures the acceleration and angular velocity of the robot in real time; processing the point cloud data acquired by the laser radar, and estimating the relative pose change of the robot; the inertial measurement unit calculates attitude information of the robot by adopting an integral algorithm according to the accelerated speed and the angular speed; performing time synchronization and data alignment on the data of the laser radar and the inertial measurement unit; and based on the positioning information of the laser radar and the attitude information provided by the inertial measurement unit, performing positioning calculation by adopting an extended Kalman filtering algorithm to obtain robot positioning. The centimeter-level positioning precision can be achieved in a complex environment, and the requirement that the robot is accurately connected with a discharging interface of an electric vehicle in a butt joint mode is met.

Description

Technical Field

[0001] The present invention relates to a mobile V2G robot positioning method, device and medium based on the fusion of laser radar and inertial measurement unit, belonging to the technical field of mobile charging robot positioning. Background Art

[0002] With the popularity of electric vehicles, vehicle-to-grid (V2G) technology has emerged. V2G technology allows electric vehicles to feed back electricity to the grid when idle, thereby improving the stability and energy efficiency of the grid. Mobile V2G robots (hereinafter referred to as robots) play an important role in this technology system. They can automatically find electric vehicles in places such as parking lots and perform discharge operations. However, the precise positioning of robots is one of the key challenges in achieving efficient V2G services.

[0003] Traditional robot positioning methods mainly include positioning methods based on single sensors such as the global positioning system (GPS), visual sensors, and lidar. These methods have their own advantages and disadvantages: GPS positioning: In outdoor environments, GPS can provide relatively rough location information. However, in areas with severe signal obstruction such as indoor parking lots, the positioning accuracy of GPS drops significantly or even cannot be used. Visual sensor positioning: This method is easily affected by environmental factors such as lighting changes and obstructions, resulting in poor robustness. Lidar positioning: Although lidar positioning has high accuracy, the positioning accuracy will gradually decrease during long-distance movement due to the accumulated error of the laser scanning point. Inertial measurement unit (IMU) positioning: IMU can measure the acceleration and angular velocity of the robot and can be used for motion estimation in a short period of time. However, when used alone, its error will accumulate rapidly over time, causing the positioning accuracy to decrease over time.

[0004] Therefore, the existing single technology is difficult to meet the robot's high-precision and high-reliability positioning requirements in complex environments. Summary of the invention

[0005] The purpose of the present invention is to provide a mobile V2G robot positioning method, device and medium based on the fusion of laser radar and inertial measurement unit, so as to overcome the shortcomings of existing positioning technology, achieve high-precision, high-stability positioning effect that can adapt to a variety of complex environments, and ensure that the robot can accurately perform operations such as discharge docking with the vehicle.

[0006] In order to achieve the above object, the present invention is implemented through the following technical solutions: A mobile V2G robot positioning method based on the fusion of laser radar and inertial measurement unit, comprising: The robot is equipped with a laser radar and an inertial measurement unit. The laser radar scans the surrounding environment and obtains point cloud data, and the inertial measurement unit measures the acceleration and angular velocity of the robot in real time. Process the point cloud data obtained by the laser radar to estimate the relative position change of the robot, including point cloud filtering, feature extraction and point cloud matching; The inertial measurement unit calculates the robot's posture information based on acceleration and angular velocity using an integration algorithm; Time synchronization and data alignment of LiDAR and IMU data; Based on the positioning information of the laser radar and the attitude information provided by the inertial measurement unit, the extended Kalman filter algorithm is used to perform positioning calculation to obtain the robot positioning.

[0007] Preferably, the point cloud filtering uses a voxel filtering algorithm to downsample the original point cloud data to reduce the amount of data and remove noise points.

[0008] Preferably, the feature extraction is to extract plane features and edge features from the filtered point cloud, calculate the covariance matrix of the points around the point and perform eigenvalue decomposition, and judge whether the point belongs to a plane feature point or an edge feature point according to the size relationship of the eigenvalues; the judgment conditions are as follows: and , then the current point is a plane feature point; and , then the current point is an edge feature point; in, are the eigenvalues ​​of the covariance matrix.

[0009] Preferably, the point cloud matching uses an iterative closest point algorithm to match the current frame point cloud with the previous frame point cloud to estimate the relative position change of the robot.

[0010] Preferably, the laser radar and the inertial measurement unit data are time synchronized and data aligned by interpolating the inertial information to the time point corresponding to the laser radar.

[0011] Preferably, the laser radar is installed at the top center of the robot to ensure a 360-degree horizontal scanning field of view.

[0012] Preferably, the inertial measurement unit is installed at a position close to the center of gravity of the robot to reduce measurement errors caused by changes in the robot's own posture during movement.

[0013] A mobile V2G robot positioning device based on the fusion of laser radar and inertial measurement unit includes a processor and a memory storing program instructions. The processor is configured to execute the mobile V2G robot positioning method based on the fusion of laser radar and inertial measurement unit when running the program instructions.

[0014] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the mobile V2G robot positioning method based on the fusion of a laser radar and an inertial measurement unit.

[0015] The advantages of the present invention are: through the complementary advantages of laser radar and inertial measurement unit, the present invention can achieve centimeter-level positioning accuracy in complex environments, meeting the needs of the robot to accurately dock with the discharge interface of the electric vehicle. At the same time, the method has strong robustness to various environmental factors, and can stably provide accurate positioning information regardless of indoor and outdoor switching, lighting changes, the presence of obstructions, etc., to ensure the continuity of the robot service. In addition, the positioning method is applicable to various types of environmental layouts, without the need for special modification or marking of parking lots, thereby reducing deployment costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0017] Figure 1 It is a schematic diagram of the process of the present invention.

[0018] Figure 2 The figure is a schematic diagram of the process of laser radar point cloud scanning, data processing and fusion of the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] Example 1 like Figure 1-Figure 2 As shown, a mobile V2G robot positioning method based on the fusion of laser radar and inertial measurement unit includes: S1: The robot is equipped with a laser radar and an inertial measurement unit. The laser radar scans the surrounding environment and obtains point cloud data, and the inertial measurement unit measures the acceleration and angular velocity of the robot in real time.

[0021] S2: Process the point cloud data obtained by the lidar to estimate the relative position change of the robot, including point cloud filtering, feature extraction and point cloud matching.

[0022] S3: The inertial measurement unit calculates the robot's posture information based on the acceleration and angular velocity using an integration algorithm.

[0023] S4: Time synchronization and data alignment between LiDAR and IMU data.

[0024] S5: Based on the positioning information of the laser radar and the attitude information provided by the inertial measurement unit, the extended Kalman filter algorithm is used for positioning calculation to obtain the robot positioning.

[0025] As a refinement of the above embodiment, the robot is equipped with a laser radar and an inertial measurement unit. The laser radar scans the surrounding environment at a specific frequency (15Hz) to obtain point cloud data; the inertial measurement unit measures the robot's acceleration in real time. and angular velocity .

[0026] As a refinement of the above embodiment, the point cloud filtering uses a voxel filtering algorithm to downsample the original point cloud data to reduce the data volume and remove noise points.

[0027] Specifically, let the original point cloud , the voxel size is , point cloud after speed-up filtering .

[0028] As a refinement of the above embodiment, the feature extraction is to extract plane features and edge features from the filtered point cloud. By calculating the covariance matrix of the points around the point and performing eigenvalue decomposition, it is determined whether the point belongs to a plane feature point or an edge feature point according to the size relationship of the eigenvalues; the judgment conditions are as follows: and , then the current point is a plane feature point; and , then the current point is an edge feature point; in, are the eigenvalues ​​of the covariance matrix.

[0029] As a refinement of the above embodiment, the point cloud matching uses an iterative closest point algorithm to match the current frame point cloud with the previous frame point cloud to estimate the relative position change of the robot.

[0030] Specifically, let the current frame point cloud , the previous point cloud , by minimizing the objective function ,(in is the rotation matrix, is the translation vector) to solve and , get the pose change estimated by the lidar .

[0031] As a refinement of the above embodiment, during data fusion, due to the different acquisition frequencies and acquisition times of the laser radar and the inertial measurement unit, time synchronization and data alignment are first required. Match the data collected by different sensors in the same time period on a unified time axis. Based on the difference between the laser radar timestamp and the acquisition frequency of the inertial measurement unit, the inertial information is interpolated to the time point corresponding to the laser radar.

[0032] As a refinement of the above embodiment, the laser radar is installed at the top center of the robot to ensure a 360-degree horizontal scanning field of view. It can fully perceive the point cloud information of the robot's surrounding environment. Its installation method uses a specially designed bracket, which is fixed to the solid structure on the top of the robot by bolts to ensure the stability and horizontality of the laser radar during the movement of the robot. At the same time, it is considered to reserve a suitable wiring channel for the data cable and power cable of the laser radar to avoid line entanglement affecting the normal operation of the robot.

[0033] As a refinement of the above embodiment, the inertial measurement unit is installed at a position close to the center of gravity of the robot to reduce the measurement error caused by the change of the robot's posture during the movement of the robot.

[0034] Specifically, the installation method is embedded in a special slot inside the chassis, and fixed to a flat part of the chassis surface through a shock-absorbing rubber pad. In this way, when the robot accelerates, decelerates or turns, the inertial measurement unit can more accurately measure the robot's acceleration and angular velocity changes, providing a reliable data source for subsequent posture calculations. At the same time, the inertial measurement unit is strictly calibrated and initialized to ensure that its measurement axis is accurately aligned with the robot's actual motion axis to improve measurement accuracy.

[0035] Optionally, visual sensors can be added to the existing LiDAR and IMU fusion. Visual sensors can provide rich texture and color information, which can assist in identifying features such as specific logos and vehicle models, further improving the robot's positioning accuracy and scene understanding capabilities in complex environments. For example, when encountering blind spots or areas with unclear features in LiDAR scanning, visual sensors can provide additional positioning references and correct positioning errors by identifying unique visual features in the surrounding environment (such as specific patterns on the ground).

[0036] It should be noted that: 1. Through the complementary advantages of LiDAR and inertial measurement unit, centimeter-level positioning accuracy can be achieved in complex environments, meeting the needs of the robot to accurately dock with the discharge interface of the electric vehicle.

[0037] 2. It is highly robust to a variety of environmental factors. Whether switching between indoor and outdoor, changing lighting, or the presence of obstructions, it can stably provide accurate positioning information to ensure the continuity of robot services.

[0038] 3. Applicable to various types of environmental layouts, no special modification or marking is required for parking lots, reducing deployment costs.

[0039] Example 2 The embodiment of the present disclosure provides a mobile V2G robot positioning device based on the fusion of laser radar and inertial measurement unit, including a processor and a memory. Optionally, the device may also include a communication interface and a bus. Among them, the processor, the communication interface, and the memory can communicate with each other through the bus. The communication interface can be used for information transmission. The processor can call the logic instructions in the memory to execute the mobile V2G robot positioning method based on the fusion of laser radar and inertial measurement unit of the above embodiment.

[0040] In addition, the logic instructions in the above-mentioned memory can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product.

[0041] The memory, as a computer-readable storage medium, can be used to store software programs and computer executable programs, such as program instructions / modules corresponding to the method in the embodiment of the present disclosure. The processor executes the functional application and data processing by running the program instructions / modules stored in the memory, that is, the mobile V2G robot positioning method based on the fusion of laser radar and inertial measurement unit in the above embodiment is realized.

[0042] The memory may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required for at least one function; the data storage area may store data created according to the use of the terminal device, etc. In addition, the memory may include a high-speed random access memory and may also include a non-volatile memory.

[0043] An embodiment of the present disclosure provides a computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are configured to execute the above-mentioned mobile V2G robot positioning method based on the fusion of laser radar and inertial measurement unit.

[0044] The computer-readable storage medium mentioned above may be a transient computer-readable storage medium or a non-transitory computer-readable storage medium.

[0045] The technical solution of the embodiment of the present disclosure can be embodied in the form of a software product, which is stored in a storage medium and includes one or more instructions for enabling a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the embodiment of the present disclosure. The aforementioned storage medium may be a non-transient storage medium, including: a USB flash drive, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a disk or an optical disk, and other media that can store program codes, or a transient storage medium.

[0046] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A mobile V2G robot positioning method based on the fusion of laser radar and inertial measurement unit, characterized in that: include: The robot is equipped with a laser radar and an inertial measurement unit. The laser radar scans the surrounding environment and obtains point cloud data, and the inertial measurement unit measures the acceleration and angular velocity of the robot in real time. Process the point cloud data obtained by the laser radar to estimate the relative position change of the robot, including point cloud filtering, feature extraction and point cloud matching; The inertial measurement unit calculates the robot's posture information based on acceleration and angular velocity using an integration algorithm; Time synchronization and data alignment of LiDAR and IMU data; Based on the positioning information of the laser radar and the attitude information provided by the inertial measurement unit, the extended Kalman filter algorithm is used to perform positioning calculation to obtain the robot positioning.

2. The mobile V2G robot positioning method based on the fusion of laser radar and inertial measurement unit according to claim 1 is characterized in that: The point cloud filtering uses a voxel filtering algorithm to downsample the original point cloud data to reduce the data volume and remove noise points.

3. The mobile V2G robot positioning method based on the fusion of laser radar and inertial measurement unit according to claim 2 is characterized in that: The feature extraction is to extract plane features and edge features from the filtered point cloud, by calculating the covariance matrix of the points around the point and performing eigenvalue decomposition, and judging whether the point belongs to a plane feature point or an edge feature point according to the size relationship of the eigenvalues; the judgment conditions are as follows: and , then the current point is a plane feature point; and , then the current point is an edge feature point; in, are the eigenvalues ​​of the covariance matrix.

4. The mobile V2G robot positioning method based on the fusion of laser radar and inertial measurement unit according to claim 3 is characterized in that: The point cloud matching uses an iterative closest point algorithm to match the current frame point cloud with the previous frame point cloud to estimate the relative position change of the robot.

5. The mobile V2G robot positioning method based on the fusion of laser radar and inertial measurement unit according to claim 1 is characterized in that: The lidar and inertial measurement unit data are time synchronized and data aligned by interpolating the inertial information to the time points corresponding to the lidar.

6. The mobile V2G robot positioning method based on the fusion of laser radar and inertial measurement unit according to claim 1 is characterized in that: The laser radar is installed at the top center of the robot to ensure a 360-degree horizontal scanning field of view.

7. The mobile V2G robot positioning method based on the fusion of laser radar and inertial measurement unit according to claim 1 is characterized in that: The inertial measurement unit is installed near the center of gravity of the robot to reduce the measurement error caused by the robot's own posture changes during movement.

8. A mobile V2G robot positioning new network-building new energy transient reactive support strategy and setting device based on the fusion of laser radar and inertial measurement unit, including a processor and a memory storing program instructions, characterized in that: The processor is configured to execute the mobile V2G robot positioning method based on the fusion of laser radar and inertial measurement unit as described in any one of claims 1-7 when running the program instructions.

9. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the program is executed by a processor, a mobile V2G robot positioning method based on the fusion of a laser radar and an inertial measurement unit as described in any one of claims 1 to 7 above is implemented.

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