Map construction optimization method, device and computer readable medium

Abstract

The present application provides a map construction optimization method, device and computer-readable medium based on generated trajectory comparison. The scheme introduces a motion constraint function to automatically generate a reference trajectory under an ideal state, and detects the accuracy of the map by comparing the difference between the reference trajectory and the actual motion trajectory, thereby reducing the dependence on manual experience and ensuring the objectivity and consistency of the detection results. At the same time, a closed-loop feedback mechanism is introduced, and the results of the difference calculation are used to adaptively optimize the synchronous positioning and map construction process, thereby ensuring robustness and accuracy in complex dynamic environments, and effectively improving the quality of map construction.

Description

Technical Field

[0001] The present application relates to the field of information technology, and in particular to a map construction optimization method, device and computer-readable medium. Background Art

[0002] In robot navigation and map building technology, the accuracy and consistency of the map are crucial to the performance of the system. Existing SLAM (Simultaneous Localization and Mapping) solutions may cause deviations in the generated map during the map building process due to sensor noise, environmental dynamics, or errors in the algorithm itself. For these deviations, a reliable method is usually required to perform quality inspection on the results of map building in order to optimize and improve the system.

[0003] However, existing SLAM systems still face many challenges in map quality detection. On the one hand, the traditional detection method based on visual inspection is highly subjective and difficult to quantify the quality of map construction; on the other hand, the detection method based on preset standard maps requires accurate benchmark data and lacks flexible adaptability to new environments. Therefore, it is difficult for existing solutions to determine objective and accurate map quality and effectively optimize the results of map construction on this basis. Summary of the invention

[0004] One object of the present application is to provide a map construction optimization method, device and computer-readable medium.

[0005] To achieve the above objectives, the present application provides a map construction optimization method based on generated trajectory comparison, the method comprising:

[0006] Acquire environmental information collected by the robot's sensors during the simultaneous positioning and map building process, and generate the robot's actual motion trajectory based on the environmental information;

[0007] According to a predefined motion constraint function, the actual motion trajectory is sampled to generate a reference trajectory;

[0008] Calculating the difference between the actual motion trajectory and the reference trajectory;

[0009] Determining whether to trigger optimization according to the difference between the actual motion trajectory and the reference trajectory;

[0010] If optimization is triggered, the relevant parameters or settings of the synchronous positioning and map building process are updated and adjusted.

[0011] Furthermore, generating an actual motion trajectory of the robot according to the environmental information includes:

[0012] Generate a key frame of the actual motion trajectory of the robot according to the environmental information, wherein the key frame includes the position and posture information of the robot at a corresponding moment;

[0013] Calculating the difference between the actual motion trajectory and the reference trajectory includes:

[0014] For each key frame in the actual motion trajectory, determining a sampling point corresponding to the key frame from the reference trajectory;

[0015] Calculate the difference between each key frame and its corresponding sampling point;

[0016] The difference between the actual motion trajectory and the reference trajectory is determined according to the difference value.

[0017] Furthermore, the difference value includes Euclidean distance, mean square error or dynamic time warping distance.

[0018] Furthermore, if optimization is triggered, the relevant parameters or settings of the synchronous positioning and map building process are updated and adjusted, including:

[0019] If optimization is triggered, the spatial distribution of the difference is obtained based on the difference value between each key frame and its corresponding sampling point;

[0020] According to the spatial distribution of the difference, identifying and marking the area to be optimized in the simultaneous positioning and map building process;

[0021] Generate feedback information about the area to be optimized, and update and adjust relevant parameters or settings of the synchronous positioning and map building process according to the feedback information.

[0022] Further, according to the spatial distribution of the difference, identifying and marking the area to be optimized in the simultaneous positioning and map building process includes:

[0023] Determine, according to the spatial distribution of the difference, a key frame in the actual motion trajectory whose difference value is higher than a preset value;

[0024] According to the position of the key frame whose difference value is higher than the preset value in the actual motion trajectory, the area to be optimized in the process of synchronous positioning and map construction is identified and marked.

[0025] Further, updating and adjusting relevant parameters or settings of the synchronous positioning and map building process according to the feedback information includes:

[0026] According to the feedback information, the sampling frequency corresponding to the area to be optimized in the synchronous positioning and map building process is increased.

[0027] Furthermore, the parameters or settings related to the simultaneous positioning and map building process are updated and adjusted, including:

[0028] Adjust the acquisition parameters of the simultaneous positioning and mapping process, update the motion model, or adjust the position of the sensor.

[0029] Further, judging whether to trigger optimization according to the difference between the actual motion trajectory and the reference trajectory includes:

[0030] The difference between the actual motion trajectory and the reference trajectory is compared with a preset difference threshold, and if the difference threshold is exceeded, it is determined that optimization is triggered.

[0031] Based on another aspect of the present application, a map construction optimization device based on generated trajectory comparison is also provided, the device comprising a memory for storing computer program instructions and a processor for executing computer program instructions, wherein when the computer program instructions are executed by the processor, the device is triggered to execute the map construction optimization method based on generated trajectory comparison.

[0032] The embodiment of the present application further provides a computer-readable medium having computer program instructions stored thereon, and the computer program instructions can be executed by a processor to implement the map construction optimization method based on generating trajectory comparison.

[0033] Compared with the prior art, the present application provides a map construction optimization solution, which first obtains the environmental information collected by the robot's sensor during the synchronous positioning and map construction process, and generates the robot's actual motion trajectory based on the environmental information; then, the actual motion trajectory can be sampled according to the predefined motion constraint function to generate a reference trajectory; then the difference between the actual motion trajectory and the reference trajectory is calculated, and whether to trigger the optimization is determined based on the difference between the actual motion trajectory and the reference trajectory; if the optimization is triggered, the relevant parameters or settings of the synchronous positioning and map construction process are updated and adjusted. This solution introduces a motion constraint function to automatically generate a reference trajectory under ideal conditions, and detects the accuracy of the map by comparing the difference between the reference trajectory and the actual motion trajectory, reducing the reliance on manual experience and ensuring the objectivity and consistency of the detection results. At the same time, a closed-loop feedback mechanism is introduced, and the result of the difference calculation is used to adaptively optimize the synchronous positioning and map construction process, ensuring robustness and accuracy in complex dynamic environments, which can effectively improve the quality of map construction. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0035] Figure 1 A flowchart of a map construction optimization method based on generated trajectory comparison provided in an embodiment of the present application;

[0036] The same or similar reference numerals in the drawings represent the same or similar components. DETAILED DESCRIPTION

[0037] The present application is described in further detail below in conjunction with the accompanying drawings.

[0038] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0039] In a typical configuration of the present application, the terminal and the equipment of the service network each include one or more processors (CPU), input / output interface, network interface and memory.

[0040] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0041] Computer readable media include permanent and non-permanent, removable and non-removable media, and can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, modules of programs or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission medium that can be used to store information that can be accessed by a computing device.

[0042] The embodiment of the present application provides a map construction optimization method based on the comparison of generated trajectories. The method first obtains the environmental information collected by the robot's sensor during the synchronous positioning and map construction process, and generates the robot's actual motion trajectory based on the environmental information; then the actual motion trajectory can be sampled according to the predefined motion constraint function to generate a reference trajectory; then the difference between the actual motion trajectory and the reference trajectory is calculated, and whether to trigger the optimization is determined based on the difference between the actual motion trajectory and the reference trajectory; if the optimization is triggered, the relevant parameters or settings of the synchronous positioning and map construction process are updated and adjusted. This method introduces a motion constraint function to automatically generate a reference trajectory under an ideal state, and detects the accuracy of the map by comparing the difference between the reference trajectory and the actual motion trajectory, thereby reducing the reliance on manual experience and ensuring the objectivity and consistency of the detection results. At the same time, a closed-loop feedback mechanism is introduced, and the result of the difference calculation is used to adaptively optimize the synchronous positioning and map construction process, ensuring robustness and accuracy in complex dynamic environments, and effectively improving the quality of map construction.

[0043] In actual scenarios, the cloud platform executing the method may include network devices, or devices formed by integrating user devices and network devices through a network, or may also be applications running on the above devices. The user devices include but are not limited to various terminal devices such as robots, computers, mobile phones, and tablet computers; the network devices include but are not limited to network hosts, single network servers, servers concentrated in multiple network servers, or servers in a distributed cloud network. The distributed cloud network described here is composed of a large number of hosts or network servers based on cloud computing.

[0044] Figure 1 The flowchart of a map construction optimization method based on trajectory generation comparison provided in an embodiment of the present application is shown, which at least includes the following processing steps:

[0045] Step S101, obtaining environmental information collected by the robot's sensors during the synchronous positioning and map building process, and generating the robot's actual motion trajectory according to the environmental information.

[0046] During the simultaneous localization and mapping (SLAM) process, the robot can detect environmental information in real time through sensors such as cameras, lidar, and IMU (Inertial Measurement Unit). This solution can obtain the environmental information collected by these sensors and use this environmental information to generate the actual motion trajectory of the robot.

[0047] Wherein, the real-time motion trajectory is represented in the form of a key frame, and the key frame includes the posture information of the robot at the corresponding moment. Therefore, when the scheme in this embodiment generates the actual motion trajectory of the robot according to the environmental information, a key frame of the actual motion trajectory of the robot can be generated according to the environmental information. In the actual scene, the posture information can adopt 6DOF (Six Degrees of Freedom, 6 degrees of freedom) posture information, including the spatial position information (x, y, z) of the object in 3D space and the rotation angle information (u, v, w) of the object around the x-axis, y-axis, and z-axis. These posture information reflects the actual motion trajectory of the robot in the process of synchronous positioning and map construction.

[0048] Step S102: sampling the actual motion trajectory according to a predefined motion constraint function to generate a reference trajectory.

[0049] In order to compare trajectories in this solution, a motion constraint function for the robot's motion behavior under ideal conditions can be predefined. The motion constraint function can be defined based on the robot's kinematic model (such as a differential drive model, a wheeled robot model) or a statistical model. According to the predefined motion constraint function, after sampling the actual motion trajectory, a reference trajectory can be obtained. This processing process is to reconstruct the robot's motion trajectory under ideal motion conditions to obtain an ideal trajectory that the robot should follow during motion without external interference, thereby obtaining a reference trajectory for comparison with the actual motion trajectory.

[0050] Step S103, calculating the difference between the actual motion trajectory and the reference trajectory. The difference is used to quantitatively represent the degree of deviation between the actual motion trajectory and the reference trajectory, thereby achieving quantitative evaluation of the map construction results, avoiding the subjectivity of manual visual inspection, and ensuring the objectivity and consistency of the map quality detection results.

[0051] In some embodiments of the present application, when calculating the difference between the actual motion trajectory and the reference trajectory, the following method can be used: for each key frame in the actual motion trajectory, the sampling point corresponding to the key frame can be determined from the reference trajectory. For example, the actual motion trajectory in the embodiment of the present application includes 100 key frames p1~p100. Taking the first key frame p1 as an example, the sampling point q1 corresponding to the key frame p1 can be determined from the reference trajectory. The sampling point can also be represented by posture information, corresponding to the spatial position and posture of the robot under ideal motion conditions without external interference. Thus, for 100 key frames p1~p100, the corresponding sampling points q1~q100 can be determined from the reference trajectory.

[0052] Then, the difference between the key frame in the actual motion trajectory and the sampling point in the reference trajectory is calculated to obtain the difference between the actual motion trajectory and the reference trajectory. In actual scenarios, the calculation process can be implemented by frame-by-frame matching calculation or by overall alignment calculation. For example, in this embodiment, the difference value between each key frame and its corresponding sampling point can be calculated, and then the difference between the actual motion trajectory and the reference trajectory can be determined based on the difference value.

[0053] When calculating the difference between the actual motion trajectory and the reference trajectory, an appropriate difference measurement method can be selected according to the needs of the actual scene, so as to more accurately quantify the difference between the actual motion trajectory and the reference trajectory. In the actual scene, assuming that the key frame of the actual motion trajectory is pi, and the corresponding sampling point on the reference trajectory is qi, when measuring the difference, the following difference function can be used:

[0054]

[0055] Where D represents the difference between the actual motion trajectory and the reference trajectory, N represents the number of key frames and corresponding sampling points, and the specific measurement method can be flexibly selected according to the needs of the actual application scenario, such as Euclidean distance, mean squared error (MSE) or dynamic time warping (DTW) distance, etc., so that this solution can meet different needs in various environments and ensure the universality and reliability of map construction quality assessment.

[0056] Step S104: determining whether to trigger optimization according to the difference between the actual motion trajectory and the reference trajectory.

[0057] Since the difference between the actual motion trajectory and the reference trajectory can be used to quantitatively evaluate the quality of map construction, specifically, a lower difference means that the deviation between the actual motion trajectory and the reference trajectory is small, that is, the robot's mapping accuracy and consistency of the environment during the SLAM process are good. A higher difference means that the deviation between the actual motion trajectory and the reference trajectory is large, indicating that the map construction result has a large error and needs further optimization.

[0058] In some embodiments of the present application, a reasonable difference threshold can be pre-set to implement optimization judgment. Specifically, when triggering optimization, the difference between the actual motion trajectory and the reference trajectory can be compared with a preset difference threshold. If it exceeds the difference threshold, it is determined to trigger optimization. For example, the difference threshold calculated in the above steps is D1, and the difference threshold is set to D0. If D1>D0, it means that the degree of deviation between the actual motion trajectory and the reference trajectory cannot meet the accuracy requirements for map construction in this solution, and optimization will be triggered. If D1≤D0, it means that the degree of deviation between the actual motion trajectory and the reference trajectory meets the accuracy requirements for map construction in this solution. At this time, the map construction result obtained can meet the accuracy requirements and no optimization is required.

[0059] Step S105: if optimization is triggered, the relevant parameters or settings of the synchronous positioning and map building process are updated and adjusted, thereby realizing a closed-loop feedback mechanism.

[0060] The specific update and adjustment methods may include adjusting the acquisition parameters of the synchronous positioning and map building process, updating the motion model, or adjusting the position of the sensor. Therefore, by introducing a closed-loop feedback mechanism, when problems are found, feedback can be given to the SLAM system in a timely manner, so that it can dynamically adjust relevant parameters or settings, thereby improving the quality of map building.

[0061] In the scheme of this embodiment, when the optimization is triggered, the relevant parameters or settings of the synchronous positioning and map building process can be updated and adjusted in the following manner. First, the spatial distribution of the difference is obtained based on the difference value between each key frame and its corresponding sampling point. Taking the aforementioned 100 groups of key frames p1~p100 and sampling points q1~q100 as an example, since each group of key frames and sampling points represents the spatial position and posture of the robot in the actual motion state and the ideal motion state, the spatial distribution of the difference of each group of key frames and sampling points is determined by frame-by-frame comparison.

[0062] After obtaining the spatial distribution of the difference, the area to be optimized in the process of synchronous positioning and map construction can be identified and marked according to the spatial distribution of the difference. For example, among 100 groups of key frames p1~p100 and sampling points q1~q100, the difference of the 50th to 60th group is significantly higher than that of other groups, so the area corresponding to the key frames p50~p60 in the actual motion trajectory can be marked as the area to be optimized.

[0063] In actual scenarios, when identifying and marking the area to be optimized in the process of synchronous positioning and map building according to the spatial distribution of the difference, the key frames with difference values ​​higher than the preset value in the actual motion trajectory can be determined according to the spatial distribution of the difference, and then the area to be optimized in the process of synchronous positioning and map building can be identified and marked according to the position of the key frames with difference values ​​higher than the preset value in the actual motion trajectory. Thus, by comparing and judging the preset values, the position in the actual motion trajectory where the error cannot meet the requirements can be accurately quantified and determined, and the area to be optimized can be obtained.

[0064] After determining the area to be optimized, feedback information about the area to be optimized can be generated, and the relevant parameters or settings of the synchronous positioning and map building process can be updated and adjusted according to the feedback information. In actual scenarios, the error of the area to be optimized may be caused by special structures in the environment (such as narrow passages, dynamic obstacles, etc.) or limitations of sensor settings. Therefore, feedback information about the area to be optimized can be generated according to actual conditions, so that the SLAM system can update and adjust the relevant parameters or settings of the synchronous positioning and map building process according to the feedback information, thereby optimizing the subsequent SLAM process and improving the quality of map construction.

[0065] For example, in some embodiments of the present application, if it is necessary to optimize the errors existing in the area to be optimized, the sampling frequency corresponding to the area to be optimized in the synchronous positioning and map construction process can be increased according to the feedback information, so that the information obtained by sampling is more accurate, which is conducive to building more accurate map data and improving the quality of map construction.

[0066] Based on the same inventive concept, a map construction optimization device based on generation trajectory comparison is also provided in an embodiment of the present application. The method corresponding to the device is the map construction optimization method based on generation trajectory comparison in the aforementioned embodiment, and its principle of solving the problem is similar to that of the method. The map construction optimization device based on generation trajectory comparison provided in an embodiment of the present application includes a memory for storing computer program instructions and a processor for executing computer program instructions, wherein when the computer program instructions are executed by the processor, the device is triggered to implement the methods and / or technical solutions of the aforementioned multiple embodiments of the present application.

[0067] The specific implementation of the cloud platform may include network devices, or devices formed by integrating user devices and network devices through a network, or may also be applications running on the above devices. The user devices include but are not limited to various terminal devices such as robots, computers, mobile phones, tablet computers, etc.; the network devices include but are not limited to network hosts, single network servers, servers concentrated in multiple network servers, or servers in a distributed cloud network. The distributed cloud network described here is composed of a large number of hosts or network servers based on cloud computing.

[0068] In particular, the methods and / or embodiments in the embodiments of the present application may be implemented as computer software programs. For example, the embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes program code for executing the method shown in the flowchart. When the computer program is executed by the processing unit, the above functions defined in the method of the present application are executed.

[0069] It should be noted that the computer-readable medium described in the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device.

[0070] In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, device, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0071] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0072] The flow chart or block diagram in the accompanying drawings shows the possible architecture, function and operation of the equipment, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated system for hardware that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0073] As another aspect, the present application further provides a computer-readable medium, which may be included in the device described in the above embodiment; or may exist independently without being assembled into the device. The above computer-readable medium carries one or more computer program instructions, which may be executed by a processor to implement the methods and / or technical solutions of the above multiple embodiments of the present application.

[0074] It should be noted that the present application can be implemented in software and / or a combination of software and hardware, for example, can be implemented using an application specific integrated circuit (ASIC), a general purpose computer or any other similar hardware device. In certain embodiments, the software program of the present application can be executed by a processor to implement the above steps or functions. Similarly, the software program of the present application (including related data structures) can be stored in a computer-readable recording medium, for example, a RAM memory, a magnetic or optical drive or a floppy disk and similar devices. In addition, some steps or functions of the present application can be implemented using hardware, for example, as a circuit that cooperates with a processor to perform each step or function.

[0075] For those skilled in the art, it is obvious that the present application is not limited to the details of the above exemplary embodiments, and the present application can be implemented in other specific forms without departing from the spirit or basic features of the present application. Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive, and the scope of the present application is limited by the attached claims rather than the above description, so it is intended to include all changes that fall within the meaning and scope of the equivalent elements of the claims in the present application. Any figure mark in the claims should not be regarded as limiting the claims involved. In addition, it is obvious that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the device claim can also be implemented by one unit or device through software or hardware. The words first, second, etc. are used to represent names, and do not represent any specific order. The numerical order of the serial numbers corresponding to the steps does not represent any specific execution order, and each step can be executed in any order combination under the premise of conforming to the execution logic.

Claims

1. A map construction optimization method based on generated trajectory comparison, characterized in that: The method comprises: Acquire environmental information collected by the robot's sensors during the simultaneous positioning and map building process, and generate the robot's actual motion trajectory based on the environmental information; According to a predefined motion constraint function, the actual motion trajectory is sampled to generate a reference trajectory; Calculating the difference between the actual motion trajectory and the reference trajectory; Determining whether to trigger optimization according to the difference between the actual motion trajectory and the reference trajectory; If optimization is triggered, the relevant parameters or settings of the synchronous positioning and map building process are updated and adjusted.

2. The method according to claim 1, characterized in that Generating an actual motion trajectory of the robot according to the environmental information includes: Generate a key frame of the actual motion trajectory of the robot according to the environmental information, wherein the key frame includes the position and posture information of the robot at a corresponding moment; Calculating the difference between the actual motion trajectory and the reference trajectory includes: For each key frame in the actual motion trajectory, determining a sampling point corresponding to the key frame from the reference trajectory; Calculate the difference between each key frame and its corresponding sampling point; The difference between the actual motion trajectory and the reference trajectory is determined according to the difference value.

3. The method according to claim 2, characterized in that The difference value includes Euclidean distance, mean square error or dynamic time warping distance.

4. The method according to claim 2, characterized in that: If optimization is triggered, the relevant parameters or settings of the synchronous positioning and map building process are updated and adjusted, including: If optimization is triggered, the spatial distribution of the difference is obtained based on the difference value between each key frame and its corresponding sampling point; According to the spatial distribution of the difference, identifying and marking the area to be optimized in the simultaneous positioning and map building process; Generate feedback information about the area to be optimized, and update and adjust relevant parameters or settings of the synchronous positioning and map building process according to the feedback information.

5. The method according to claim 4, characterized in that According to the spatial distribution of the difference, the area to be optimized in the simultaneous positioning and map building process is identified and marked, including: Determine, according to the spatial distribution of the difference, a key frame in the actual motion trajectory whose difference value is higher than a preset value; According to the position of the key frame whose difference value is higher than the preset value in the actual motion trajectory, the area to be optimized in the process of synchronous positioning and map construction is identified and marked.

6. The method according to claim 4, characterized in that The relevant parameters or settings of the synchronous positioning and map building process are updated and adjusted according to the feedback information, including: According to the feedback information, the sampling frequency corresponding to the area to be optimized in the synchronous positioning and map building process is increased.

7. The method according to claim 4, characterized in that Updates and adjustments to parameters or settings related to the simultaneous positioning and map building process, including: Adjust the acquisition parameters of the simultaneous positioning and mapping process, update the motion model, or adjust the position of the sensor.

8. The method according to claim 1, characterized in that: Determining whether to trigger optimization according to the difference between the actual motion trajectory and the reference trajectory includes: The difference between the actual motion trajectory and the reference trajectory is compared with a preset difference threshold, and if the difference threshold is exceeded, it is determined that optimization is triggered.

9. A map building optimization device based on generating trajectory comparison, the device comprising a memory for storing computer program instructions and a processor for executing the computer program instructions, wherein: When the computer program instructions are executed by the processor, the device is triggered to execute the method according to any one of claims 1 to 8.

10. A computer readable medium having computer program instructions stored thereon, wherein the computer program instructions can be executed by a processor to implement the method according to any one of claims 1 to 8.

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