Laser SLAM Mapping Method, Apparatus, Electronic Device, and Computer Readable Storage Medium

Abstract

The present application discloses a laser SLAM mapping method, apparatus, electronic device, and computer-readable storage medium. The method includes: obtaining mapping data collected by a vehicle end in a preset positioning area, where the preset positioning area is an area without available satellite positioning signals, and the mapping data includes high-precision inertial navigation data and laser point cloud data; performing post-processing on the high-precision inertial navigation data, and the post-processed high-precision inertial navigation data includes high-precision inertial navigation poses; constructing factors of a preset graph optimization algorithm according to the post-processed high-precision inertial navigation data and the laser point cloud data, specifically including a laser odometry factor and a high-precision inertial navigation factor; performing optimization based on the factors of the preset graph optimization algorithm to obtain optimized poses, and constructing a map based on this. The present application optimizes the existing laser SLAM mapping solution. By constructing a high-precision inertial navigation factor to participate in the optimization process of the preset graph optimization algorithm, the degradation phenomenon that occurs in laser SLAM mapping in specific scenarios is avoided, and the mapping accuracy of laser SLAM is improved.

Description

Technical Field

[0001] The present application relates to the technical field of map construction, and particularly to a method and device for laser SLAM mapping, an electronic device, and a computer-readable storage medium. Background Art

[0002] The Simultaneous Localization and Mapping (SLAM) technology can accurately achieve environmental map construction, positioning, and multi-point navigation. The current SLAM technology can be divided into Lidar SLAM and Visual SLAM. The sensor used in Lidar SLAM is a lidar, while the Visual SLAM uses a depth camera. The Lidar SLAM technology is relatively mature, with less error, and is sufficient to meet the current environmental use.

[0003] The realization of Lidar SLAM mapping mainly depends on feature points in the environment. When the feature points in the environment are rich enough, the Lidar SLAM mapping can achieve good mapping accuracy. However, in some special scenarios, the number of feature points that can be extracted from the environment is small. For example, in a tunnel scenario, the number of feature points is very small and relatively single, which will lead to the degradation of the existing Lidar SLAM mapping scheme, and then greatly reduce the accuracy of Lidar SLAM mapping, making it difficult to meet the mapping requirements in such scenarios and unable to be used for subsequent positioning. Summary of the Invention

[0004] Embodiments of the present application provide a method and device for laser SLAM mapping, an electronic device, and a computer-readable storage medium to improve the accuracy of laser SLAM mapping in special scenarios.

[0005] Embodiments of the present application adopt the following technical solutions:

[0006] In a first aspect, embodiments of the present application provide a method for laser SLAM mapping, where the method includes:

[0007] Obtain mapping data collected by the vehicle end in a preset positioning area, where the preset positioning area is an area without available satellite positioning signals, and the mapping data includes high-precision inertial navigation data and lidar point cloud data;

[0008] Post-process the high-precision inertial navigation data to obtain post-processed high-precision inertial navigation data, where the post-processed high-precision inertial navigation data includes high-precision inertial navigation poses;

[0009] According to the post-processed high-precision inertial navigation data and the lidar point cloud data, construct factors of a preset graph optimization algorithm, where the factors of the preset graph optimization algorithm include a lidar odometry factor and a high-precision inertial navigation factor;

[0010] Optimize based on the factors of the preset map optimization algorithm to obtain the optimized pose, and construct a map according to the optimized pose.

[0011] Optionally, the post-processed high-precision inertial navigation data further includes IMU data. The construction of the factors of the preset map optimization algorithm based on the post-processed high-precision inertial navigation data and the laser point cloud data includes:

[0012] Perform IMU pre-integration processing on the IMU data to obtain an IMU pre-integration factor as a factor of the preset map optimization algorithm;

[0013] The optimization based on the factors of the preset map optimization algorithm to obtain the optimized pose includes:

[0014] Optimize based on the IMU pre-integration factor, the laser odometry factor, and the high-precision inertial navigation factor to obtain the optimized pose.

[0015] Optionally, the optimization based on the IMU pre-integration factor, the laser odometry factor, and the high-precision inertial navigation factor to obtain the optimized pose includes:

[0016] Based on the high-precision inertial navigation factor, adjust the pose confidence of the IMU pre-integration factor, the laser odometry factor, and the high-precision inertial navigation factor;

[0017] Optimize according to the pose confidence of the adjusted IMU pre-integration factor, the pose confidence of the adjusted laser odometry factor, and the pose confidence of the adjusted high-precision inertial navigation factor to obtain the optimized pose.

[0018] Optionally, the post-processed high-precision inertial navigation data further includes IMU data. The construction of the factors of the preset map optimization algorithm based on the post-processed high-precision inertial navigation data and the laser point cloud data includes:

[0019] Use the IMU data to perform distortion removal processing on the laser point cloud data to obtain the distortion-removed laser point cloud data;

[0020] Extract corner features and surface features from the distortion-removed laser point cloud data;

[0021] Construct the laser odometry factor according to the extracted corner features and surface features.

[0022] Optionally, before optimizing based on the factors of the preset map optimization algorithm to obtain the optimized pose, the method further includes:

[0023] Perform loop detection according to the laser point cloud data;

[0024] Construct a loop factor according to the loop detection result as a factor of the preset map optimization algorithm.

[0025] Optionally, the mapping data further includes wheel speed data. Before optimizing based on the factor of the preset map optimization algorithm to obtain the optimized pose, the method further includes:

[0026] Determine the position data corresponding to each frame of wheel speed data based on the wheel speed data;

[0027] Construct a wheel speed factor according to the position data corresponding to each frame of wheel speed data as a factor of the preset map optimization algorithm.

[0028] Optionally, after optimizing based on the factor of the preset map optimization algorithm to obtain the optimized pose and constructing a map according to the optimized pose, the method further includes:

[0029] Compare the optimized pose with the high-precision inertial navigation pose;

[0030] Determine the mapping accuracy of the optimized pose according to the comparison result;

[0031] Return the mapping accuracy of the optimized pose to the vehicle terminal so that the vehicle terminal determines whether to reconstruct the map according to the mapping accuracy of the optimized pose.

[0032] In a second aspect, an embodiment of the present application further provides a laser SLAM mapping device, where the device includes:

[0033] An acquisition unit, configured to acquire mapping data collected by a vehicle terminal in a preset positioning area, where the preset positioning area is an area without available satellite positioning signals, and the mapping data includes high-precision inertial navigation data and laser point cloud data;

[0034] A post-processing unit, configured to post-process the high-precision inertial navigation data to obtain post-processed high-precision inertial navigation data, and the post-processed high-precision inertial navigation data includes a high-precision inertial navigation pose;

[0035] A first construction unit, configured to construct factors of a preset map optimization algorithm according to the post-processed high-precision inertial navigation data and the laser point cloud data, and the factors of the preset map optimization algorithm include a laser odometry factor and a high-precision inertial navigation factor;

[0036] An optimization unit, configured to optimize based on the factors of the preset map optimization algorithm to obtain an optimized pose and construct a map according to the optimized pose.

[0037] In a third aspect, an embodiment of the present application further provides an electronic device, including:

[0038] A processor; and

[0039] A memory arranged to store computer-executable instructions that, when executed, cause the processor to execute any one of the foregoing methods.

[0040] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium storing one or more programs that, when executed by an electronic device including a plurality of application programs, cause the electronic device to execute any one of the foregoing methods.

[0041] The above at least one technical solution adopted in the embodiments of the present application can achieve the following beneficial effects: In the laser SLAM mapping method of the embodiments of the present application, first, mapping data collected by the vehicle end in a preset positioning area is acquired. The preset positioning area is an area without available satellite positioning signals, and the mapping data includes high-precision inertial navigation data and laser point cloud data; then, the high-precision inertial navigation data is post-processed to obtain the post-processed high-precision inertial navigation data, and the post-processed high-precision inertial navigation data includes high-precision inertial navigation poses; then, according to the post-processed high-precision inertial navigation data and the laser point cloud data, factors of a preset graph optimization algorithm are constructed, and the factors of the preset graph optimization algorithm include a laser odometry factor and a high-precision inertial navigation factor; finally, optimization is performed based on the factors of the preset graph optimization algorithm to obtain the optimized poses, and a map is constructed according to the optimized poses. The laser SLAM mapping method of the embodiments of the present application optimizes the laser SLAM mapping scheme for a specific positioning area. By constructing a high-precision inertial navigation factor to participate in the optimization process of the preset graph optimization algorithm, the degradation phenomenon of laser SLAM mapping caused by a small number of feature points in a specific scenario is avoided, and the mapping accuracy of laser SLAM is improved. Description of the Drawings

[0042] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments and descriptions thereof of the present application are used to explain the present application and do not constitute an improper limitation of the present application. In the drawings:

[0043] Figure 1 It is a schematic flowchart of a laser SLAM mapping method in an embodiment of the present application;

[0044] Figure 2 It is a schematic diagram of the laser SLAM mapping effect in a tunnel scenario in an embodiment of the present application;

[0045] Figure 3 It is a schematic structural diagram of a laser SLAM mapping device in an embodiment of the present application;

[0046] Figure 4 It is a schematic structural diagram of an electronic device in an embodiment of the present application. Detailed implementation manners

[0047] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments of this application and the corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of this application.

[0048] The following will describe in detail the technical solutions provided by each embodiment of this application with reference to the drawings.

[0049] An embodiment of this application provides a laser SLAM mapping method. As Figure 1 shown, a flowchart of a laser SLAM mapping method in an embodiment of this application is provided. The method at least includes the following steps S110 to S140:

[0050] Step S110: Obtain mapping data collected by the vehicle end in a preset positioning area. The preset positioning area is an area where no available satellite positioning signal exists. The mapping data includes high-precision inertial navigation data and laser point cloud data.

[0051] The laser SLAM mapping method of the embodiment of this application can be executed by the cloud. When performing laser SLAM mapping, it is necessary to first obtain the mapping data collected by the vehicle end in the preset positioning area. Here, the preset positioning area refers to an area where the satellite positioning signal is unavailable, and the number of feature points in these areas is small.

[0052] The above mapping data mainly includes high-precision inertial navigation data and laser point cloud data collected in the entire preset positioning area. To avoid the degradation phenomenon that occurs due to the small number of feature points extracted from the laser point cloud data in a specific scenario, the embodiment of this application can use a high-precision inertial navigation device installed on the vehicle end to collect high-precision inertial navigation positioning data. Compared with traditional inertial navigation devices, it can reduce the cumulative error of the positioning data that increases with time.

[0053] Step S120: Post-process the high-precision inertial navigation data to obtain the post-processed high-precision inertial navigation data. The post-processed high-precision inertial navigation data includes high-precision inertial navigation poses.

[0054] After obtaining the above high-precision inertial navigation data, it is also necessary to post-process the high-precision inertial navigation data to further improve the accuracy of the high-precision inertial navigation data. Here, the post-processing operation mainly resolves the high-precision inertial navigation data collected by the high-precision inertial navigation device to obtain data such as position, attitude, and speed.

[0055] Step S130: Based on the post-processed high-precision inertial navigation data and the laser point cloud data, construct the factors of a preset graph optimization algorithm, where the factors of the preset graph optimization algorithm include a laser odometry factor and a high-precision inertial navigation factor.

[0056] In the embodiments of the present application, a preset graph optimization algorithm can be used to optimize the pose of map building. Specifically, the factor graph optimization method can be adopted. Therefore, after obtaining the post-processed high-precision inertial navigation data, it is necessary to construct the corresponding high-precision inertial navigation factor according to the post-processed high-precision inertial navigation data, and a corresponding lidar odometry factor can be constructed according to the laser point cloud data as the basis for subsequent optimization.

[0057] In addition, since the output data frequencies of different sensors are different, for the convenience of subsequent processing, a certain time synchronization algorithm such as interpolation method can also be used here to perform time synchronization processing on the post-processed high-precision inertial navigation data and the laser point cloud data.

[0058] Step S140: Optimize based on the factors of the preset graph optimization algorithm to obtain the optimized pose, and construct a map according to the optimized pose.

[0059] Based on the above laser odometry factor and high-precision inertial navigation factor, the pose can be optimized to obtain the optimized pose. Furthermore, based on the optimized pose, the laser point cloud data can be stitched together to obtain the constructed laser point cloud map. By introducing the optimization constraint of the high-precision inertial navigation factor, the degradation phenomenon of the laser odometry factor due to the small number of feature points can be avoided, and the mapping error is reduced.

[0060] The laser SLAM mapping method of the embodiments of the present application optimizes the laser SLAM mapping scheme for a specific positioning area. By constructing a high-precision inertial navigation factor to participate in the optimization process of the preset graph optimization algorithm, the degradation phenomenon of laser SLAM mapping caused by the small number of feature points in a specific scenario is avoided, and the mapping accuracy of laser SLAM is improved.

[0061] In some embodiments of the present application, the post-processed high-precision inertial navigation data further includes IMU data. The constructing the factors of the preset graph optimization algorithm according to the post-processed high-precision inertial navigation data and the laser point cloud data includes: performing IMU pre-integration processing based on the IMU data to obtain an IMU pre-integration factor as the factor of the preset graph optimization algorithm; the optimizing based on the factors of the preset graph optimization algorithm to obtain the optimized pose includes: optimizing based on the IMU pre-integration factor, the laser odometry factor, and the high-precision inertial navigation factor to obtain the optimized pose.

[0062] The post-processed high-precision inertial navigation data in the embodiments of the present application further includes IMU data. Here, the IMU data may refer to the IMU data after removing errors such as zero bias and accelerometer zero bias. Through the IMU data, pre-integration can be calculated to obtain pose transformation, and thus the IMU pre-integration factor can be constructed. The IMU pre-integration factor, the lidar odometry factor, and the high-precision inertial navigation factor are jointly used as factors in a preset map optimization algorithm for backend optimization to obtain the optimized pose, thereby further improving the optimization efficiency and effect.

[0063] In some embodiments of the present application, the optimization based on the IMU pre-integration factor, the lidar odometry factor, and the high-precision inertial navigation factor to obtain the optimized pose includes: based on the high-precision inertial navigation factor, adjusting the pose confidence of the IMU pre-integration factor, the lidar odometry factor, and the high-precision inertial navigation factor; and performing optimization according to the adjusted pose confidence of the IMU pre-integration factor, the adjusted pose confidence of the lidar odometry factor, and the adjusted pose confidence of the high-precision inertial navigation factor to obtain the optimized pose.

[0064] When the embodiments of the present application perform pose optimization based on the IMU pre-integration factor, the lidar odometry factor, and the high-precision inertial navigation factor, since the covariance sizes corresponding to different factors are different, the corresponding pose confidences in the optimization process are also different. The larger the covariance, the lower the pose confidence, and the smaller the covariance, the higher the corresponding pose confidence.

[0065] Since the high-precision inertial navigation factor in the embodiments of the present application is constructed based on the high-precision inertial navigation data collected by a high-precision inertial navigation device, it does not depend on the number and richness of feature points extracted from the environment, and there is no problem that the cumulative error increases with time. Therefore, its corresponding covariance should be smaller, and thus a higher pose confidence can be given to it in the pose optimization process, while reducing the pose confidences corresponding to the IMU pre-integration factor and the lidar odometry factor. That is, compared with the IMU pre-integration factor and the lidar odometry factor, there is a greater probability of believing the pose corresponding to the high-precision inertial navigation factor.

[0066] The above process is essentially, in the process of pose optimization, adaptively adjusting the covariance size or confidence size of different optimization factors based on the high-precision inertial navigation factor constructed in the foregoing embodiments, so that the factor with higher precision occupies a higher weight in the optimization process, and the factor with lower precision occupies a relatively smaller weight in the optimization process, which is more applicable to mapping scenarios with a small number of feature points such as tunnels.

[0067] In addition, it should be noted that since the covariance size of the IMU pre-integration factor is mainly affected by time accumulation, the adjustment of the confidence size of the IMU pre-integration factor can gradually decrease with time accumulation.

[0068] In some embodiments of the present application, the post-processed high-precision inertial navigation data further includes IMU data. The factors for constructing a preset graph optimization algorithm based on the post-processed high-precision inertial navigation data and the lidar point cloud data include: using the IMU data to perform distortion correction on the lidar point cloud data to obtain the distortion-corrected lidar point cloud data; extracting corner features and surface features from the distortion-corrected lidar point cloud data; and constructing the lidar odometry factor based on the extracted corner features and surface features.

[0069] In the embodiments of the present application, when constructing the lidar odometry factor, the lidar point cloud data can be first subjected to distortion correction. Specifically, the rotation increment can be calculated using the IMU data corresponding to the current frame of lidar point cloud data, and the translation increment can be calculated using IMU pre-integration. Then, motion distortion correction is performed on each lidar point of this frame of lidar point cloud data at each moment. At the same time, the pose of the current frame of lidar point cloud data is roughly initialized using the attitude angle of the IMU data and the pose corresponding to the IMU pre-integration, thereby completing the distortion correction of the lidar point cloud data.

[0070] After that, for the lidar point cloud data after motion distortion correction, the curvature of each point is calculated. Then, corner features and surface features can be extracted based on the magnitude of the curvature, and the pose of the lidar point cloud data can be calculated based on the corner features and surface features, so as to construct the lidar odometry factor.

[0071] In some embodiments of the present application, before optimizing based on the factors of the preset graph optimization algorithm to obtain the optimized pose, the method further includes: performing loop detection based on the lidar point cloud data; constructing a loop factor according to the loop detection result as a factor of the preset graph optimization algorithm.

[0072] In the embodiments of the present application, when implementing lidar SLAM mapping in a specific scenario, the optimization efficiency and optimization effect can be further improved by adding loop constraints. In the early stage, the vehicle can be controlled to drive along the loop trajectory, and the mapping data collected in this way can be used for subsequent loop detection. Specifically, existing loop detection algorithms such as the bag-of-words model or methods based on deep learning can be used for loop detection, so that a pose constraint relationship can be established based on the loop detection result, and combined with other factors to jointly perform pose optimization, improving the pose optimization efficiency and optimization effect.

[0073] In some embodiments of the present application, the mapping data further includes wheel speed data. Before optimizing based on the factors of the preset graph optimization algorithm to obtain the optimized pose, the method further includes: determining the position data corresponding to each frame of wheel speed data based on the wheel speed data; constructing a wheel speed factor according to the position data corresponding to each frame of wheel speed data as a factor of the preset graph optimization algorithm.

[0074] In specific mapping scenarios such as tunnels, the wheel speed data collected by the wheel speedometer on the vehicle is less affected by external factors and has relatively high accuracy. Therefore, in the embodiments of the present application, a wheel speed factor can be further constructed based on the wheel speed data, thereby providing richer constraint information for subsequent pose optimization.

[0075] Specifically, in the mapping scenario, it can be considered that both the lateral speed and the radial speed in the wheel speed data collected by the wheel speedometer are 0, that is, there is only the speed in the forward direction. Since the initial position of the vehicle before entering the preset positioning area is known, then based on the speed in the forward direction collected by the wheel speedometer in real time, the corresponding positions at each moment can be calculated. Based on these position data, position constraints can be constructed, thereby providing more reference information for the subsequent optimization process and further improving the optimization efficiency and effect.

[0076] In some embodiments of the present application, after optimizing based on the factors of the preset map optimization algorithm to obtain the optimized pose and constructing a map according to the optimized pose, the method further includes: comparing the optimized pose with the high-precision inertial navigation pose; determining the mapping accuracy of the optimized pose according to the comparison result; and returning the mapping accuracy of the optimized pose to the vehicle end so that the vehicle end can determine whether to reconstruct the map according to the mapping accuracy of the optimized pose.

[0077] In order to evaluate the mapping quality, in the embodiments of the present application, after obtaining the optimized pose, the optimized pose for mapping and the high-precision inertial navigation data can also be compared. Since the accuracy of the high-precision inertial navigation data is relatively high, the corresponding pose data can be used as a basis for judging the error size of the optimized pose.

[0078] According to the comparison result of the two, the mapping accuracy of the optimized pose can be determined. For example, if the horizontal angle error in the attitude angle is less than 0.1, the heading angle error is less than 0.2, and at the same time the corresponding statistical error is less than twice the standard deviation (2sigma), and the position error is less than 0.15m, and at the same time the corresponding statistical error is less than twice the standard deviation (2sigma), it is considered that the accuracy is relatively high. In this way, the mapping result can be scored, or the mapping result can be expressed in other forms to indicate whether it meets the preset accuracy requirements, and the result is returned to the vehicle end. The vehicle end can use this to judge whether secondary mapping is required. As Figure 2 shown, a schematic diagram of the laser SLAM mapping effect in a tunnel scenario in the embodiments of the present application is provided.

[0079] In summary, the laser SLAM mapping method of the present application has at least achieved the following technical effects:

[0080] 1) Based on the vehicle-cloud integrated framework, laser SLAM mapping in special scenarios such as tunnels is realized, and the mapping efficiency is relatively high;

[0081] 2) Optimize the existing laser SLAM mapping scheme based on high-precision inertial navigation data, which has high mapping accuracy and better meets the mapping requirements in special scenarios such as tunnels;

[0082] 3) Evaluate the mapping effect based on high-precision inertial navigation data, ensure the mapping accuracy and effect, and provide strong support for subsequent vehicle positioning.

[0083] The embodiment of the present application also provides a laser SLAM mapping device 300, as Figure 3 shown, which provides a structural schematic diagram of a laser SLAM mapping device in the embodiment of the present application. The device 300 at least includes: an acquisition unit 310, a post-processing unit 320, a first construction unit 330, and an optimization unit 340, where:

[0084] The acquisition unit 310 is used to acquire mapping data collected by the vehicle end in a preset positioning area, where the preset positioning area is an area without available satellite positioning signals, and the mapping data includes high-precision inertial navigation data and laser point cloud data;

[0085] The post-processing unit 320 is used to post-process the high-precision inertial navigation data to obtain post-processed high-precision inertial navigation data, and the post-processed high-precision inertial navigation data includes high-precision inertial navigation poses;

[0086] The first construction unit 330 is used to construct factors of a preset graph optimization algorithm according to the post-processed high-precision inertial navigation data and the laser point cloud data, and the factors of the preset graph optimization algorithm include a laser odometry factor and a high-precision inertial navigation factor;

[0087] The optimization unit 340 is used to optimize based on the factors of the preset graph optimization algorithm to obtain optimized poses, and construct a map according to the optimized poses.

[0088] In some embodiments of the present application, the post-processed high-precision inertial navigation data further includes IMU data. The first construction unit 330 is specifically used for: performing IMU pre-integration processing based on the IMU data to obtain an IMU pre-integration factor as a factor of the preset graph optimization algorithm; the optimization unit is specifically used for: optimizing based on the IMU pre-integration factor, the laser odometry factor, and the high-precision inertial navigation factor to obtain optimized poses.

[0089] In some embodiments of the present application, the optimization unit 340 is specifically configured to: adjust the pose confidence of the IMU pre-integration factor, the lidar odometry factor, and the high-precision inertial navigation factor based on the high-precision inertial navigation factor; perform optimization according to the pose confidence of the adjusted IMU pre-integration factor, the pose confidence of the adjusted lidar odometry factor, and the pose confidence of the adjusted high-precision inertial navigation factor to obtain an optimized pose.

[0090] In some embodiments of the present application, the post-processed high-precision inertial navigation data further includes IMU data, and the first construction unit 330 is specifically configured to: perform distortion removal processing on the lidar point cloud data using the IMU data to obtain the distortion-removed lidar point cloud data; extract corner features and surface features from the distortion-removed lidar point cloud data; construct the lidar odometry factor according to the extracted corner features and surface features.

[0091] In some embodiments of the present application, the device further includes: a loop detection unit for performing loop detection according to the lidar point cloud data; a second construction unit for constructing a loop factor according to the loop detection result as a factor of the preset graph optimization algorithm.

[0092] In some embodiments of the present application, the device further includes: a first determination unit for determining position data corresponding to each frame of wheel speed data based on the wheel speed data; a third construction unit for constructing a wheel speed factor according to the position data corresponding to each frame of wheel speed data as a factor of the preset graph optimization algorithm.

[0093] In some embodiments of the present application, the device further includes: a comparison unit for comparing the optimized pose with the high-precision inertial navigation pose; a second determination unit for determining the mapping accuracy of the optimized pose according to the comparison result; a return unit for returning the mapping accuracy of the optimized pose to the vehicle end so that the vehicle end determines whether to re-map according to the mapping accuracy of the optimized pose.

[0094] It can be understood that the above lidar SLAM mapping device can implement each step of the lidar SLAM mapping method provided in the foregoing embodiments. The related explanations of the lidar SLAM mapping method are applicable to the lidar SLAM mapping device and will not be elaborated here.

[0095] Figure 4 This is a schematic structural diagram of an electronic device according to an embodiment of the present application. Please refer to Figure 4, at the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and a memory. Among them, the memory may include memory, such as high-speed random access memory (RAM), and may also include non-volatile memory, such as at least one disk memory, etc. Of course, the electronic device may also include other hardware required for other services.

[0096] The processor, network interface, and memory can be interconnected through the internal bus, and the internal bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the sake of representation, Figure 4 only a bidirectional arrow is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0097] The memory is used to store programs. Specifically, the program can include program code, and the program code includes computer operation instructions. The memory can include memory and non-volatile memory, and provide instructions and data to the processor.

[0098] The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it, forming a laser SLAM mapping device at the logical level. The processor executes the program stored in the memory and is specifically used to perform the following operations:

[0099] Obtain mapping data collected by the vehicle end in a preset positioning area, where the preset positioning area is an area without available satellite positioning signals, and the mapping data includes high-precision inertial navigation data and laser point cloud data;

[0100] Perform post-processing on the high-precision inertial navigation data to obtain post-processed high-precision inertial navigation data, and the post-processed high-precision inertial navigation data includes high-precision inertial navigation poses;

[0101] According to the post-processed high-precision inertial navigation data and the laser point cloud data, construct factors of a preset graph optimization algorithm, and the factors of the preset graph optimization algorithm include a laser odometry factor and a high-precision inertial navigation factor;

[0102] Perform optimization based on the factors of the preset graph optimization algorithm to obtain an optimized pose, and perform map construction according to the optimized pose.

[0103] As described in the present application Figure 1 The method executed by the laser SLAM mapping device disclosed in the embodiments shown above can be applied to or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by the integrated logic circuit of the hardware in the processor or the instructions in the form of software. The above-mentioned processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. It can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present application. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in combination with the embodiments of the present application can be directly embodied as being executed and completed by the hardware decoding processor, or executed and completed by a combination of the hardware and software modules in the decoding processor. The software module may be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. This storage medium is located in the memory, and the processor reads the information in the memory and combines its hardware to complete the steps of the above method.

[0104] The electronic device can also execute Figure 1 the method executed by the laser SLAM mapping device in Figure 1 the embodiments shown, and implement the functions of the laser SLAM mapping device in the embodiments shown. The embodiments of the present application will not be elaborated here.

[0105] The embodiments of the present application also propose a computer-readable storage medium. The computer-readable storage medium stores one or more programs. The one or more programs include instructions that, when executed by an electronic device including multiple application programs, can enable the electronic device to execute Figure 1 the method executed by the laser SLAM mapping device in the embodiments shown, and specifically used to execute:

[0106] Obtain mapping data collected by the vehicle end in a preset positioning area. The preset positioning area is an area without available satellite positioning signals. The mapping data includes high-precision inertial navigation data and laser point cloud data;

[0107] Perform post - processing on the high - precision inertial navigation data to obtain the post - processed high - precision inertial navigation data, where the post - processed high - precision inertial navigation data includes high - precision inertial navigation pose;

[0108] According to the post - processed high - precision inertial navigation data and the laser point cloud data, construct factors of a preset graph optimization algorithm, where the factors of the preset graph optimization algorithm include a laser odometry factor and a high - precision inertial navigation factor;

[0109] Perform optimization based on the factors of the preset graph optimization algorithm to obtain the optimized pose, and construct a map according to the optimized pose.

[0110] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer - usable storage media (including but not limited to disk memory, CD - ROM, optical memory, etc.) containing computer - usable program code.

[0111] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the processes and / or blocks in the flowchart and / or block diagram can also be implemented. These computer program instructions can be provided to the processor of a general - purpose computer, a special - purpose computer, an embedded processor, or other programmable data - processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data - processing devices generate means for implementing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0112] These computer program instructions can also be stored in a computer - readable memory that can direct a computer or other programmable data - processing device to work in a specific manner, so that the instructions stored in the computer - readable memory generate a manufactured article including instruction means, and the instruction means implements the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0113] These computer program instructions can also be loaded onto a computer or other programmable data - processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer - implemented process. Thus, the instructions executed on the computer or other programmable device provide means for implementing the functions specified in one process Figure 1A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0114] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0115] 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.

[0116] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules 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, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0117] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0118] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0119] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. A laser SLAM mapping method, wherein, The method includes: Obtaining mapping data collected by the vehicle end in a preset positioning area, where the preset positioning area is an area without available satellite positioning signals, and the mapping data includes high-precision inertial navigation data and lidar point cloud data; Performing post-processing on the high-precision inertial navigation data to obtain post-processed high-precision inertial navigation data, where the post-processed high-precision inertial navigation data includes high-precision inertial navigation poses; Constructing factors of a preset graph optimization algorithm based on the post-processed high-precision inertial navigation data and the lidar point cloud data, where the factors of the preset graph optimization algorithm include a lidar odometry factor and a high-precision inertial navigation factor; Performing optimization based on the factors of the preset graph optimization algorithm to obtain optimized poses, and constructing a map according to the optimized poses.

2. The method according to claim 1, wherein The post-processed high-precision inertial navigation data further includes IMU data, and constructing the factors of the preset graph optimization algorithm based on the post-processed high-precision inertial navigation data and the lidar point cloud data includes: Performing IMU pre-integration processing on the IMU data to obtain an IMU pre-integration factor as a factor of the preset graph optimization algorithm; The performing optimization based on the factors of the preset graph optimization algorithm to obtain optimized poses includes: Performing optimization based on the IMU pre-integration factor, the lidar odometry factor, and the high-precision inertial navigation factor to obtain optimized poses.

3. The method according to claim 2, wherein, The performing optimization based on the IMU pre-integration factor, the lidar odometry factor, and the high-precision inertial navigation factor to obtain optimized poses includes: Based on the high-precision inertial navigation factor, adjusting the pose confidence levels of the IMU pre-integration factor, the lidar odometry factor, and the high-precision inertial navigation factor; Performing optimization according to the adjusted pose confidence level of the IMU pre-integration factor, the adjusted pose confidence level of the lidar odometry factor, and the adjusted pose confidence level of the high-precision inertial navigation factor to obtain optimized poses.

4. The method according to claim 1, wherein, The post-processed high-precision inertial navigation data further includes IMU data, and constructing the factors of the preset graph optimization algorithm based on the post-processed high-precision inertial navigation data and the lidar point cloud data includes: Using the IMU data to perform distortion removal processing on the lidar point cloud data to obtain distortion-removed lidar point cloud data; Extracting corner features and surface features from the distortion-removed lidar point cloud data; Constructing the lidar odometry factor according to the extracted corner features and surface features.

5. The method according to claim 1, wherein, Before performing optimization based on the factors of the preset graph optimization algorithm to obtain optimized poses, the method further includes: Performing loop detection according to the lidar point cloud data; Constructing a loop factor according to the loop detection result as a factor of the preset graph optimization algorithm.

6. The method according to claim 1, wherein The mapping data further includes wheel speed data. Before performing optimization based on the factors of the preset graph optimization algorithm to obtain optimized poses, the method further includes: Determining position data corresponding to each frame of wheel speed data based on the wheel speed data; Constructing a wheel speed factor according to the position data corresponding to each frame of wheel speed data as a factor of the preset graph optimization algorithm.

7. The method according to claim 1, wherein After performing optimization based on the factors of the preset graph optimization algorithm to obtain optimized poses and constructing a map according to the optimized poses, the method further includes: Compare the optimized pose with the high-precision inertial navigation pose; Determine the mapping accuracy of the optimized pose according to the comparison result; Return the mapping accuracy of the optimized pose to the vehicle terminal, so that the vehicle terminal determines whether to reconstruct the map according to the mapping accuracy of the optimized pose.

8. A laser SLAM mapping device, wherein, The device includes: An acquisition unit, configured to acquire mapping data collected by a vehicle terminal in a preset positioning area, where the preset positioning area is an area without available satellite positioning signals, and the mapping data includes high-precision inertial navigation data and laser point cloud data; A post-processing unit, configured to perform post-processing on the high-precision inertial navigation data to obtain post-processed high-precision inertial navigation data, where the post-processed high-precision inertial navigation data includes a high-precision inertial navigation pose; A first construction unit, configured to construct factors of a preset map optimization algorithm according to the post-processed high-precision inertial navigation data and the laser point cloud data, where the factors of the preset map optimization algorithm include a laser odometry factor and a high-precision inertial navigation factor; An optimization unit, configured to perform optimization based on the factors of the preset map optimization algorithm to obtain an optimized pose, and construct a map according to the optimized pose.

9. An electronic device, including: A processor; And A memory arranged to store computer-executable instructions, and the executable instructions, when executed, cause the processor to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, where the computer-readable storage medium stores one or more programs, and when the one or more programs are executed by an electronic device including a plurality of application programs, the electronic device is caused to execute the method according to any one of claims 1 to 7.

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