Adaptive mapping method based on scene recognition, electronic equipment and mobile robot

Abstract

The invention provides an adaptive mapping method based on scene recognition, electronic equipment and a mobile robot, and the method comprises the steps: obtaining the environment point cloud data of a target scene through a laser radar, and obtaining the motion data of the mobile robot through an inertial measurement unit; judging whether the target scene is a degraded scene based on the environment point cloud data of the target scene; adjusting a first fusion weight of the laser radar and a second fusion weight of the inertial measurement unit on the basis of a judgment result of whether the target scene is a degraded scene, determining a fusion pose of the mobile robot on the basis of the first fusion weight and the second fusion weight, and determining a degradation state of the mobile robot on the basis of the fusion pose, the environmental point cloud data and the motion data. And constructing an environment map of the target scene. According to the invention, the fusion weight of the laser radar and the inertial measurement unit can be dynamically adjusted based on different scene types, so that the environment map can be accurately constructed.

Description

Technical Field

[0001] This disclosure relates to the field of robotics, and more particularly to an adaptive mapping method based on scene recognition, an electronic device, and a mobile robot. Background Technology

[0002] With the continuous development of intelligent robot technology, autonomous localization and mapping of robots has become an important research direction. In existing technologies, LiDAR and inertial measurement units (IMUs) are commonly used sensors for robot localization and navigation. However, in complex environments, how to dynamically adjust the fusion strategy of LiDAR and IMUs according to the characteristics of different scenarios to improve the accuracy and stability of mapping remains a challenge.

[0003] Current adaptive mapping methods typically rely on adjustments to adapt to changes in the environment. However, when faced with specific degraded scenarios (such as long straight corridors or open squares), existing methods are less adaptable and may lead to inaccurate robot localization in these scenarios, affecting navigation performance.

[0004] Therefore, an adaptive mapping method based on scene recognition is proposed, which can automatically adjust the weights of sensors according to the characteristics of the environment to better complete the mapping task. Summary of the Invention

[0005] To address the aforementioned problems, this disclosure provides an improved map construction method to at least partially resolve these issues.

[0006] According to a first aspect of this disclosure, an adaptive mapping method based on scene recognition is provided, applied to a mobile robot equipped with a lidar and an inertial measurement unit (IMU), comprising: acquiring environmental point cloud data of a target scene through the lidar and acquiring motion data of the mobile robot through the IMU; determining whether the target scene is a degraded scene based on the environmental point cloud data of the target scene; adjusting a first fusion weight of the lidar and a second fusion weight of the IMU based on the determination result of whether the target scene is a degraded scene, wherein if the target scene is a degraded scene, the second fusion weight is higher than the first fusion weight, and if the target scene is a non-degraded scene, the first fusion weight is higher than the second fusion weight; determining the fused pose of the mobile robot based on the first fusion weight and the second fusion weight, and constructing an environmental map of the target scene based on the fused pose, the environmental point cloud data, and the motion data.

[0007] According to a second aspect of this disclosure, a mobile control method based on a mobile robot is provided, comprising: constructing an environment map based on the method described in the first aspect; mapping the real-time pose of the mobile robot onto the environment map; adjusting the movement path of the mobile robot according to the mapping result of the real-time pose in the environment map and a preset movement path of the currently executed task, and generating corresponding control commands; and controlling the mobile robot to perform movement based on the control commands.

[0008] According to a third aspect of this disclosure, a mobile robot is provided, comprising: a mobile base; a lidar and an inertial measurement unit disposed on the mobile base; and a control module for performing the method as described in the first or second aspect.

[0009] According to a fourth aspect of this disclosure, an electronic device is provided, comprising: a processor; and a memory storing a program; wherein the program includes instructions that, when executed by the processor, cause the processor to perform the method described in the first aspect above.

[0010] According to a fifth aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions, wherein the computer instructions are configured to cause a computer to perform the method described in the first aspect above.

[0011] In summary, the adaptive mapping method, motion control method, and device provided in this disclosure can solve the problem of poor mapping performance of robots in different scenarios in the prior art. By dynamically adjusting the weights of the LiDAR and the inertial measurement unit, the robot can construct environmental maps more accurately. Attached Figure Description

[0012] The following figures are intended only to illustrate and explain this disclosure and do not limit the scope of this disclosure. Figure 1 This is a flowchart illustrating an adaptive mapping method based on scene recognition, which is an exemplary embodiment of this disclosure.

[0013] Figure 2 This is a flowchart illustrating a mobile robot movement control method according to an exemplary embodiment of the present disclosure.

[0014] Figure 3 This is a structural block diagram of a mobile robot that is an exemplary embodiment of the present disclosure.

[0015] Figure 4 This is a structural block diagram of an electronic device that is an exemplary embodiment of the present disclosure. Detailed Implementation

[0016] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0017] It should be understood that the steps described in the method embodiments of this disclosure may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of this disclosure is not limited in this respect.

[0018] The term "comprising" and its variations as used herein are open-ended, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the description below. It should be noted that the concepts of "first", "second", etc., used in this disclosure are only used to distinguish different devices, modules, or units, and are not intended to limit the order of functions performed by these devices, modules, or units or their interdependencies.

[0019] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative and not restrictive. Those skilled in the art should understand that, unless explicitly stated in the context, they should be understood as "one or more". The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of these messages or information.

[0020] Current adaptive mapping methods typically rely on adjustments to adapt to changes in the environment. However, when faced with specific degraded scenarios (such as long straight corridors or open squares), existing methods are less adaptable and may lead to inaccurate robot localization in these scenarios, affecting navigation performance.

[0021] In view of this, the embodiments of this disclosure propose an adaptive mapping method based on scene recognition, which can automatically adjust the fusion weights of each sensor according to the characteristics of the environmental scene, so as to construct a scene map more accurately.

[0022] The specific implementation of the embodiments of this disclosure will be described in detail below with reference to the accompanying drawings.

[0023] Adaptive mapping method based on scene recognition Figure 1The present disclosure illustrates the processing flow of an adaptive mapping method based on scene recognition, applicable to mobile robots equipped with LiDAR and inertial measurement units, and specifically includes the following steps: Step 102: Obtain environmental point cloud data of the target scene through LiDAR, and obtain motion data of the mobile robot through inertial measurement unit.

[0024] LiDAR (Light Detection and Ranging) can accurately measure the distance to a target by emitting high-frequency laser pulses and receiving the signals reflected back from surrounding objects, using the Time of Flight (ToF) principle. Combined with the scanning angle information of the laser beam, environmental point cloud data of the target scene can be constructed in three-dimensional space to characterize the geometric structure of the target scene, such as building outlines, road boundaries, and obstacle shapes.

[0025] An inertial measurement unit (IMU) typically consists of a three-axis accelerometer and a three-axis gyroscope, and some may also integrate a magnetometer to continuously output the linear acceleration and angular velocity of the mobile robot body.

[0026] Step 104: Based on the environmental point cloud data of the target scene, determine whether the target scene is a degraded scene.

[0027] In this embodiment, a degraded scene typically refers to an area lacking sufficient environmental features. For example, a long, straight corridor, an open plaza, or an area with a single type of environmental feature.

[0028] In some embodiments, environmental point cloud data at adjacent time points can be matched to calculate point cloud matching stability parameters; based on the point cloud matching stability parameters, it can be determined whether the target scene is a degraded scene.

[0029] In this embodiment, the point cloud matching stability parameters include at least one of point cloud matching residual, feature point distribution uniformity, and pose covariance matrix. Point cloud matching residual refers to the statistical measure of the distance between corresponding point pairs after point cloud registration (such as root mean square error (RMSE), mean absolute error (MAE), etc.), used to measure the degree to which two frames of point clouds fail to align completely under optimal pose transformation. In scenarios with rich structure and obvious features, the matching residual is usually small; however, in degraded scenarios, due to the lack of effective constraints, the optimization algorithm may converge to local minima or incorrect solutions, leading to a significant increase in residual. Feature point distribution uniformity is used to evaluate the coverage and dispersion of these key points in space, for example, by calculating the distribution entropy and standard deviation of feature points in the azimuth angle or spatial quadrant, or using the coverage index after spherical projection. In non-degenerate scenarios, feature points are typically distributed across multiple directions, providing comprehensive geometric constraints. However, in degenerate scenarios (such as facing a long wall), feature points may be concentrated in a narrow region, causing pose estimation to be unobservable in certain degrees of freedom (such as rotation around the normal or translation along the wall). The magnitude of the eigenvalues ​​of the covariance matrix directly reflects the observability of the system in each direction. If an eigenvalue is abnormally large (or the condition number is too high), it indicates that the corresponding degree of freedom lacks effective constraints, and the system is in a degenerate state.

[0030] In practical applications, if the matching residual is high and the feature points are concentrated in a single direction, and the pose covariance is extremely large in the yaw angle direction, then the target scene can be determined to be a degraded scene.

[0031] In other embodiments, the environmental point cloud data of the target scene can be predicted by a prediction model to obtain the number of effective feature points and the confidence value of point cloud matching; if the number of effective feature points is lower than a preset effective number threshold, or the confidence value of point cloud matching is lower than a confidence threshold, the prediction result that the target scene is a degraded scene is obtained.

[0032] In this embodiment, the effective feature points include at least one of corner points, edge points, and planar feature points.

[0033] By constructing a predictive model, the number of effective feature points and matching confidence of environmental point cloud data of the target scene are jointly evaluated, and whether it is a degraded scene is determined according to a preset threshold. This enables a dynamic and quantitative understanding of environmental observability, providing key support for highly robust autonomous systems.

[0034] Step 106: Based on the judgment result of whether the target scene is a degraded scene, adjust the first fusion weight of the lidar and the second fusion weight of the inertial measurement unit.

[0035] If the target scene is a degraded scene, the second fusion weight is higher than the first fusion weight; if the target scene is a non-degraded scene, the first fusion weight is higher than the second fusion weight.

[0036] When the target scene is determined to be a degraded scene, the fusion weight of the motion data of the inertial measurement unit in the pose estimation of the mobile robot can be increased, while the fusion weight of the environmental point cloud data of the lidar can be reduced.

[0037] When the target scene is determined to be a non-degradable scene, the fusion weight of environmental point cloud data from LiDAR in the pose estimation of the mobile robot can be increased. Furthermore, the pose of the mobile robot can be predicted by integrating the acceleration and angular velocity data output by the inertial measurement unit.

[0038] By dynamically adjusting the fusion weights of LiDAR and inertial measurement unit in pose estimation according to the degree of scene degradation, the "environmental adaptive perception" capability of mobile robots can be realized.

[0039] Step 108: Based on the first fusion weight and the second fusion weight, determine the fused pose of the mobile robot, and construct an environmental map of the target scene based on the fused pose, environmental point cloud data and motion data.

[0040] In some embodiments, a continuous function smooth transition technique can be used to dynamically calculate the first fusion weight and the second fusion weight to determine the fusion pose of the mobile robot.

[0041] Therefore, the technical solution in this embodiment achieves intelligent collaboration between LiDAR and inertial measurement unit in pose estimation through a dynamic fusion weight mechanism, and constructs a highly consistent environmental map based on this. It not only solves the positioning failure problem in degraded scenarios, but also improves the system's autonomous mapping capability across all scenarios.

[0042] Mobile robot movement control methods Figure 2 This is a flowchart illustrating the motion control method for a mobile robot according to an exemplary embodiment of the present disclosure, which mainly includes: Step 202: Construct an environment map.

[0043] In this embodiment, it can be achieved through Figure 1 The aforementioned adaptive mapping method constructs a scene map.

[0044] Step 204: Map the real-time pose of the mobile robot onto the environment map.

[0045] During the movement of the mobile robot, the LiDAR and inertial measurement unit are activated in real time to continuously collect relevant data to obtain the real-time pose of the mobile robot. Specifically, the inertial measurement unit is used to collect the pitch angle, roll angle and yaw angle of the mobile robot in real time to obtain the robot's real-time attitude information; the LiDAR is used to continuously collect environmental point cloud data of the surrounding environment and match the collected environmental point cloud data with the environmental map constructed in step 202.

[0046] The system can register real-time environmental point cloud data acquired by LiDAR with grid data in the environmental map to obtain the initial pose of the mobile robot relative to the environmental map. A Kalman filter algorithm is then used to fuse and optimize the robot's translational velocity, attitude information, and initial pose, eliminating noise interference from single sensor data and obtaining a precise real-time pose (including real-time position coordinates and real-time attitude angles). Subsequently, the precise real-time pose is mapped according to the grid coordinate system of the environmental map to determine the specific grid position of the mobile robot in the environmental map, and the mapped position of the mobile robot in the environmental map is updated in real time.

[0047] Step 206: Based on the mapping result of the real-time pose in the environment map and the preset movement path of the current task, adjust the movement path of the mobile robot and generate corresponding control commands.

[0048] It can acquire the real-time mapping result of the mobile robot in the environmental map, i.e., the real-time grid position, and compare the real-time grid position with the corresponding path points on the preset movement path to calculate the deviation value. It determines whether the deviation value exceeds a preset threshold. If it does not exceed the threshold, the preset movement path remains unchanged; if it does exceed the threshold, a path adjustment algorithm is activated to adjust the movement path.

[0049] Based on the adjusted movement path, corresponding control commands can be generated, such as the left wheel speed command, right wheel speed command, and direction control command for the mobile robot. The left wheel speed command and right wheel speed command are calculated based on the adjusted movement speed and path curvature, while the direction control command is used to control the mobile robot to turn according to the newly planned path.

[0050] Step 208: Control the mobile robot to move based on control commands.

[0051] In some embodiments, control commands include movement path adjustment commands, obstacle avoidance commands, or task switching commands.

[0052] This embodiment achieves precise movement control of the mobile robot in complex environments through the above steps, effectively coping with obstacles and path deviations in the environment, and ensuring the stability and reliability of task execution.

[0053] Mobile robots Figure 3 A structural block diagram of a mobile robot 300 according to an exemplary embodiment of this disclosure, which mainly includes: Mobile base 302; The sensing module 304 is mounted on the mobile base 302 and includes a lidar and an inertial measurement unit. The control module 306 is used to execute the adaptive mapping method or the mobile robot movement control method described in the above embodiments.

[0054] Exemplary embodiments of this disclosure also provide an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor. The memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to cause the electronic device to perform methods according to embodiments of this disclosure.

[0055] Exemplary embodiments of this disclosure also provide a non-transitory computer-readable storage medium storing a computer program, wherein the computer program, when executed by a computer's processor, is used to cause the computer to perform methods according to various embodiments of this disclosure.

[0056] Exemplary embodiments of this disclosure also provide a computer program product, including a computer program, wherein, when executed by a processor of a computer, the computer program is used to cause the computer to perform methods according to various embodiments of this disclosure.

[0057] refer to Figure 4 The present invention describes a structural block diagram of an electronic device 400 that can serve as a server or client of the present disclosure, which is an example of a hardware device that can be applied to various aspects of the present disclosure. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptops, 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, 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 present disclosure described and / or claimed herein.

[0058] like Figure 4As shown, the electronic device 400 includes a computing unit 401, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 402 or a computer program loaded from a storage unit 408 into a random access memory (RAM) 403. The RAM 403 may also store various programs and data required for the operation of the device 400. The computing unit 401, ROM 402, and RAM 403 are interconnected via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0059] Multiple components in electronic device 400 are connected to I / O interface 405, including: input unit 406, output unit 407, storage unit 408, and communication unit 409. Input unit 406 can be any type of device capable of inputting information to electronic device 400. Input unit 406 can receive input digital or character information and generate key signal inputs related to user settings and / or function control of electronic device. Output unit 407 can be any type of device capable of presenting information and may include, but is not limited to, a display, speaker, video / audio output terminal, vibrator, and / or printer. Storage unit 408 may include, but is not limited to, disks and optical discs. Communication unit 409 allows electronic device 400 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers, and / or chipsets, such as Bluetooth™ devices, WiFi devices, WiMax devices, cellular communication devices, and / or the like.

[0060] The computing unit 401 can be a variety of general-purpose and / or dedicated processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 401 performs the various methods and processes described above. For example, in some embodiments, the scene recognition-based adaptive mapping method or the mobile robot motion control method of the foregoing embodiments can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 408. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 400 via ROM 402 and / or communication unit 409. In some embodiments, the computing unit 401 can be configured by any other suitable means (e.g., by means of firmware) to perform the scene recognition-based adaptive mapping method or the mobile robot motion control method.

[0061] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0062] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. 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 fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0063] As used in this disclosure, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, device, and / or apparatus (e.g., disk, optical disk, memory, programmable logic device (PLD)) for providing machine instructions and / or data to a programmable processor, including machine-readable media that receive machine instructions as machine-readable signals. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor.

[0064] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. 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).

[0065] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user 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., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0066] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other.

[0067] It should be understood that although this specification is described according to various embodiments, not every embodiment contains only one independent technical solution. This way of describing the specification is only for clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

[0068] The above descriptions are merely illustrative embodiments of this disclosure and are not intended to limit the scope of this disclosure. Any equivalent changes, modifications, and combinations made by those skilled in the art without departing from the concept and principles of this disclosure should fall within the protection scope of this disclosure.

Claims

1. An adaptive mapping method based on scene recognition, applicable to mobile robots equipped with LiDAR and inertial measurement units, comprising: The laser radar acquires environmental point cloud data of the target scene, and the inertial measurement unit acquires motion data of the mobile robot. Based on the environmental point cloud data of the target scene, determine whether the target scene is a degraded scene; Based on the determination result of whether the target scene is a degraded scene, the first fusion weight of the lidar and the second fusion weight of the inertial measurement unit are adjusted. If the target scene is a degraded scene, the second fusion weight is higher than the first fusion weight; if the target scene is a non-degraded scene, the first fusion weight is higher than the second fusion weight. Based on the first fusion weight and the second fusion weight, the fused pose of the mobile robot is determined, and based on the fused pose, the environmental point cloud data, and the motion data, an environmental map of the target scene is constructed.

2. The adaptive mapping method according to claim 1, wherein, The step of determining whether the target scene is a degraded scene based on the environmental point cloud data of the target scene includes: Match environmental point cloud data at adjacent time points and calculate point cloud matching stability parameters; Based on the point cloud matching stability parameters, determine whether the target scene is a degraded scene; The point cloud matching stability parameters include at least one of point cloud matching residuals, feature point distribution uniformity, and pose covariance matrix.

3. The adaptive mapping method according to claim 1, wherein, The step of determining whether the target scene is a degraded scene based on the environmental point cloud data of the target scene includes: By using a prediction model, the environmental point cloud data of the target scene is predicted to obtain the effective feature point count and point cloud matching confidence value. If the number of effective feature points is lower than a preset effective number threshold, or the confidence value of point cloud matching is lower than a confidence threshold, the prediction result that the target scene is a degraded scene is obtained. The effective feature points include at least one of corner points, edge points, and planar feature points.

4. The adaptive mapping method according to claim 1, wherein, The degraded scenarios include long, straight corridors, open squares, or areas with a single distribution of environmental features.

5. The adaptive mapping method according to claim 1 further includes: When the target scene is determined to be a non-degradable scene, the pose estimation result of the inertial measurement unit is corrected using the environmental point cloud data.

6. The adaptive mapping method according to claim 1 further includes: When the target scene is determined to be a non-degradable scene, the pose of the mobile robot is predicted by integral using the acceleration and angular velocity data output by the inertial measurement unit.

7. A method for controlling the movement of a mobile robot, comprising: An environmental map is constructed based on the method of any one of claims 1 to 4; The real-time pose of the mobile robot is mapped onto the environmental map; Based on the mapping result of the real-time pose in the environment map and the preset movement path of the current task, the movement path of the mobile robot is adjusted and corresponding control commands are generated. The mobile robot is controlled to move based on the control commands.

8. The motion control method according to claim 7, wherein, The control commands include movement path adjustment commands, obstacle avoidance commands, or task switching commands.

9. A mobile robot, comprising: Mobile base; The sensing module is mounted on the mobile base and includes a lidar and an inertial measurement unit; A control module for performing the method as described in any one of claims 1 to 8.

10. An electronic device, comprising: The processor, memory, communication interface, and communication bus are provided, wherein the processor, memory, and communication interface communicate with each other via the communication bus. The memory is used to store at least one executable instruction that causes the processor to perform an operation corresponding to the method as described in any one of claims 1 to 8.

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