CN115014332A

CN115014332A - Laser SLAM mapping method and device, electronic equipment and computer readable storage medium

Info

Publication number: CN115014332A
Application number: CN202210774636.6A
Authority: CN
Inventors: 费再慧; 李岩; 张海强
Original assignee: Zhidao Network Technology Beijing Co Ltd
Current assignee: Zhidao Network Technology Beijing Co Ltd
Priority date: 2022-07-01
Filing date: 2022-07-01
Publication date: 2022-09-06

Abstract

The application discloses a laser SLAM mapping method, a device, an electronic device and a computer readable storage medium, wherein the method comprises the following steps: acquiring mapping data acquired by a vehicle end in a preset positioning area, wherein the preset positioning area is an area without available satellite positioning signals, and the mapping data comprises high-precision inertial navigation data and laser point cloud data; post-processing the high-precision inertial navigation data, wherein the post-processed high-precision inertial navigation data comprises a high-precision inertial navigation pose; constructing factors of a preset graph optimization algorithm according to the post-processed high-precision inertial navigation data and the laser point cloud data, wherein the factors specifically comprise laser odometer factors and high-precision inertial navigation factors; and optimizing based on factors of a preset map optimization algorithm to obtain an optimized pose, and constructing a map according to the pose. The method and the device optimize the existing laser SLAM mapping scheme, participate in the optimization process of the preset mapping optimization algorithm by constructing the high-precision inertial navigation factor, avoid the degradation phenomenon of the laser SLAM mapping in a specific scene, and improve the mapping precision of the laser SLAM.

Description

Laser SLAM mapping method and device, electronic equipment and computer readable storage medium

Technical Field

The present disclosure relates to the field of map construction technologies, and in particular, to a method and an apparatus for laser SLAM map construction, an electronic device, and a computer-readable storage medium.

Background

The synchronous positioning and Mapping (SLAM) technology can accurately realize environment Mapping, positioning and multipoint navigation. Current SLAM technologies can be divided into laser SLAM (lidar SLAM) which uses a laser radar as a sensor, and visual SLAM (visual SLAM) which uses a depth camera. The laser SLAM technology is mature, has few errors and is enough to meet the use requirement of the current environment.

The realization of the laser SLAM mapping mainly depends on the feature points in the environment, when the feature points in the environment are abundant enough, the laser SLAM mapping can obtain good mapping precision, however, under some special scenes, the number of the feature points which can be extracted from the environment is small, for example, under a tunnel scene, the number of the feature points is small and relatively single, which causes the degradation phenomenon of the existing laser SLAM mapping scheme, and further causes the precision of the laser SLAM mapping to be greatly reduced, the mapping requirement under the scene is difficult to meet, and the laser SLAM mapping can not be used for subsequent positioning.

Disclosure of Invention

The embodiment of the application provides a laser SLAM mapping method and device, electronic equipment and a computer readable storage medium, so as to improve the mapping precision of the laser SLAM in a special scene.

The embodiment of the application adopts the following technical scheme:

in a first aspect, an embodiment of the present application provides a laser SLAM mapping method, where the method includes:

acquiring mapping data acquired by a vehicle end in a preset positioning area, wherein the preset positioning area is an area without available satellite positioning signals, and the mapping data comprises high-precision inertial navigation data and laser point cloud data;

post-processing the high-precision inertial navigation data to obtain post-processed high-precision inertial navigation data, wherein the post-processed high-precision inertial navigation data comprises a high-precision inertial navigation pose;

constructing factors of a preset graph optimization algorithm according to the post-processed high-precision inertial navigation data and the laser point cloud data, wherein the factors of the preset graph optimization algorithm comprise laser odometer factors and high-precision inertial navigation factors;

and optimizing the factors based on the preset map optimization algorithm to obtain an optimized pose, and constructing a map according to the optimized pose.

Optionally, the post-processed high-precision inertial navigation data further includes IMU data, and the constructing a factor of a preset map 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 a factor of the preset map optimization algorithm;

the factor based on the preset map optimization algorithm is optimized, and obtaining the optimized pose comprises the following steps:

and optimizing based on the IMU pre-integration factor, the laser odometer factor and the high-precision inertial navigation factor to obtain an optimized pose.

Optionally, the optimizing based on the IMU pre-integration factor, the laser odometer factor, and the high-precision inertial navigation factor to obtain an optimized pose includes:

adjusting pose confidence coefficients of the IMU pre-integration factor, the laser odometry factor and the high-precision inertial navigation factor based on the high-precision inertial navigation factor;

and optimizing according to the pose confidence coefficient of the adjusted IMU pre-integral factor, the pose confidence coefficient of the adjusted laser odometer factor and the pose confidence coefficient of the adjusted high-precision inertial navigation factor to obtain the optimized pose.

carrying out distortion removal processing on the laser point cloud data by utilizing the IMU data to obtain the laser point cloud data after distortion removal;

extracting angular point features and surface features in the undistorted laser point cloud data;

and constructing the laser odometry factor according to the extracted corner feature and the extracted surface feature.

Optionally, before optimizing based on the factor of the preset map optimization algorithm to obtain an optimized pose, the method further includes:

performing loop detection according to the laser point cloud data;

and constructing a loop factor according to a loop detection result, wherein the loop factor is used as a factor of the preset graph optimization algorithm.

Optionally, the map building data further includes wheel speed data, and before optimizing based on a factor of the preset map optimization algorithm to obtain an optimized pose, the method further includes:

determining position data corresponding to each frame of wheel speed data based on the wheel speed data;

and constructing a wheel speed factor according to the position data corresponding to each frame of wheel speed data, wherein the wheel speed factor is used as a factor of the preset map optimization algorithm.

Optionally, after optimizing the preset map optimization algorithm based on the factor to obtain an optimized pose and performing map construction according to the optimized pose, the method further includes:

comparing the optimized pose with the high-precision inertial navigation pose;

determining the mapping precision of the optimized pose according to the comparison result;

and returning the mapping precision of the optimized pose to the vehicle end so that the vehicle end can determine whether to rebuild the map according to the mapping precision of the optimized pose.

In a second aspect, an embodiment of the present application further provides a laser SLAM mapping apparatus, where the apparatus includes:

the system comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring mapping data acquired by a vehicle end in a preset positioning area, the preset positioning area is an area without available satellite positioning signals, and the mapping data comprises high-precision inertial navigation data and laser point cloud data;

the post-processing unit is used for post-processing the high-precision inertial navigation data to obtain post-processed high-precision inertial navigation data, and the post-processed high-precision inertial navigation data comprises a high-precision inertial navigation pose;

the first construction unit is used for constructing factors of a preset map optimization algorithm according to the post-processed high-precision inertial navigation data and the laser point cloud data, wherein the factors of the preset map optimization algorithm comprise laser odometer factors and high-precision inertial navigation factors;

and the optimization unit is used for optimizing based on the factors of the preset map optimization algorithm to obtain an optimized pose and constructing a map according to the optimized pose.

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

a processor; and

a memory arranged to store computer executable instructions that, when executed, cause the processor to perform any of the methods described above.

In a fourth aspect, embodiments of the present application further provide 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 perform any of the methods described above.

The embodiment of the application adopts at least one technical scheme which can achieve the following beneficial effects: the laser SLAM mapping method comprises the steps of firstly obtaining mapping data collected by a vehicle end in a preset positioning area, wherein the preset positioning area is an area without available satellite positioning signals, and the mapping data comprises high-precision inertial navigation data and laser point cloud data; then, post-processing the high-precision inertial navigation data to obtain post-processed high-precision inertial navigation data, wherein the post-processed high-precision inertial navigation data comprises a high-precision inertial navigation pose; then, according to the post-processed high-precision inertial navigation data and the laser point cloud data, constructing factors of a preset map optimization algorithm, wherein the factors of the preset map optimization algorithm comprise laser odometer factors and high-precision inertial navigation factors; and finally, optimizing based on factors of a preset map optimization algorithm to obtain an optimized pose, and constructing a map according to the optimized pose. The laser SLAM mapping method provided by the embodiment of the application is optimized according to the laser SLAM mapping scheme in the specific positioning area, and participates in the optimization process of the preset map optimization algorithm by constructing the high-precision inertial navigation factor, so that the degradation phenomenon of laser SLAM mapping caused by the small number of feature points in a specific scene is avoided, and the mapping precision of the laser SLAM is improved.

Drawings

The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:

fig. 1 is a schematic flowchart of a laser SLAM mapping method in an embodiment of the present application;

fig. 2 is a schematic diagram illustrating a laser SLAM mapping effect in a tunnel scene in an embodiment of the present application;

fig. 3 is a schematic structural diagram of a laser SLAM mapping apparatus in an embodiment of the present application;

fig. 4 is a schematic structural diagram of an electronic device in an embodiment of the present application.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail and completely with reference to the following specific embodiments of the present application and the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

The technical solutions provided by the embodiments of the present application are described in detail below with reference to the accompanying drawings.

The embodiment of the present application provides a laser SLAM mapping method, and as shown in fig. 1, provides a schematic flow chart of the laser SLAM mapping method in the embodiment of the present application, where the method at least includes the following steps S110 to S140:

step S110, obtaining mapping data acquired by a vehicle end in a preset positioning area, wherein the preset positioning area is an area without available satellite positioning signals, and the mapping data comprises high-precision inertial navigation data and laser point cloud data.

The laser SLAM mapping method can be executed by a cloud end, when the laser SLAM mapping is carried out, mapping data acquired by a vehicle end in a preset positioning area need to be acquired firstly, the preset positioning area refers to an area where satellite positioning signals are unavailable, and the number of characteristic points in the areas is small.

The mapping data mainly comprises high-precision inertial navigation data and laser point cloud data which are acquired in the whole preset positioning area. In order to avoid the degradation phenomenon that appears because the characteristic point quantity that extracts from the laser point cloud data is less under specific scene, the high accuracy inertial navigation positioning data can be gathered to the high accuracy inertial navigation equipment that this application embodiment can utilize car end installation, compares in traditional inertial navigation equipment, can reduce the accumulative error of positioning data grow along with time.

And S120, post-processing the high-precision inertial navigation data to obtain post-processed high-precision inertial navigation data, wherein the post-processed high-precision inertial navigation data comprises a high-precision inertial navigation pose.

After the high-precision inertial navigation data are obtained, post-processing is further required to be performed on the high-precision inertial navigation data to further improve the precision of the high-precision inertial navigation data, wherein the post-processing operation is mainly to solve the high-precision inertial navigation data acquired by the high-precision inertial navigation equipment to obtain data such as position, attitude, speed and the like.

And S130, constructing factors of a preset map optimization algorithm according to the post-processed high-precision inertial navigation data and the laser point cloud data, wherein the factors of the preset map optimization algorithm comprise laser odometer factors and high-precision inertial navigation factors.

According to the method and the device, the pose of the map building can be optimized by adopting a preset map optimization algorithm, and particularly, a factor graph optimization mode can be adopted, so that after the post-processed high-precision inertial navigation data is obtained, corresponding high-precision inertial navigation factors need to be built according to the post-processed high-precision inertial navigation data, and corresponding laser radar odometer factors can be built according to the laser point cloud data and serve as the basis of subsequent optimization.

In addition, because the frequencies of the data output by different sensors are different, in order to facilitate subsequent processing, certain time synchronization algorithms such as interpolation can be used for performing time synchronization processing on the post-processed high-precision inertial navigation data and the laser point cloud data.

And S140, optimizing based on the factors of the preset map optimization algorithm to obtain an optimized pose, and constructing a map according to the optimized pose.

And optimizing the pose based on the laser odometer factor and the high-precision inertial navigation factor to obtain the optimized pose, and splicing the laser point cloud data based on the optimized pose to obtain the constructed laser point cloud map. By introducing optimization constraints of high-precision inertial navigation factors, the degradation phenomenon of laser odometer factors due to the small number of feature points can be avoided, and the map building error is reduced.

The laser SLAM mapping method provided by the embodiment of the application is optimized according to the laser SLAM mapping scheme in the specific positioning area, and participates in the optimization process of the preset map optimization algorithm by constructing the high-precision inertial navigation factor, so that the degradation phenomenon of laser SLAM mapping caused by the small number of feature points in a specific scene is avoided, and the mapping precision of the laser SLAM is improved.

In some embodiments of the present application, the post-processed high-precision inertial navigation data further includes IMU data, and constructing a factor of a preset map 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 a factor of the preset map optimization algorithm; the factor based on the preset map optimization algorithm is optimized, and the obtaining of the optimized pose comprises the following steps: and optimizing based on the IMU pre-integration factor, the laser odometer factor and the high-precision inertial navigation factor to obtain an optimized pose.

The post-processed high-precision inertial navigation data further comprises IMU data, wherein the IMU data can be IMU data without errors such as zero offset, table zero offset and the like, pre-integration can be calculated through the IMU data, pose transformation is obtained, and therefore IMU pre-integration factors can be constructed. And performing back-end optimization by taking the IMU pre-integration factor, the laser odometer factor and the high-precision inertial navigation factor as the factors of a preset map optimization algorithm to obtain an optimized pose, so that the optimization efficiency and the optimization effect are further improved.

In some embodiments of the present application, the optimizing based on the IMU pre-integration factor, the laser odometry factor, and the high-precision inertial navigation factor to obtain the optimized pose includes: adjusting pose confidence coefficients of the IMU pre-integration factor, the laser odometry factor and the high-precision inertial navigation factor based on the high-precision inertial navigation factor; and optimizing according to the pose confidence coefficient of the adjusted IMU pre-integral factor, the pose confidence coefficient of the adjusted laser odometer factor and the pose confidence coefficient of the adjusted high-precision inertial navigation factor to obtain the optimized pose.

When the pose optimization is carried out based on the IMU pre-integration factor, the laser odometer factor and the high-precision inertial navigation factor, due to the fact that the covariance degrees corresponding to different factors are different, the corresponding pose confidence degrees in the optimization process are different, the larger the covariance is, the lower the pose confidence degree is, and the smaller the covariance is, the higher the corresponding pose confidence degree is.

The high-precision inertial navigation factor is constructed based on high-precision inertial navigation data acquired by high-precision inertial navigation equipment, does not depend on the number and abundance of characteristic points extracted from the environment, and does not have the problem of large accumulated error along with time, so that the corresponding covariance is smaller, a higher pose confidence coefficient can be given to the high-precision inertial navigation factor in the pose optimization process, and meanwhile, the pose confidence coefficient corresponding to the IMU pre-integration factor and the laser odometer factor is reduced, namely, compared with the IMU pre-integration factor and the laser odometer factor, the pose corresponding to the high-precision inertial navigation factor is believed with a higher probability.

The process is essentially that in the process of pose optimization, the high-precision inertial navigation factors constructed based on the embodiment adaptively adjust the covariance magnitude or the confidence degree magnitude of different optimization factors, so that the factors with higher precision occupy higher weight in the optimization process, the factors with lower precision occupy relatively smaller weight in the optimization process, and the method is more suitable for the mapping scene with smaller number of characteristic points such as tunnels and the like.

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

In some embodiments of the present application, the post-processed high-precision inertial navigation data further includes IMU data, and constructing a factor of a preset map optimization algorithm according to the post-processed high-precision inertial navigation data and the laser point cloud data includes: carrying out distortion removal processing on the laser point cloud data by using the IMU data to obtain the laser point cloud data after distortion removal; extracting angular point features and surface features in the undistorted laser point cloud data; and constructing the laser odometry factor according to the extracted corner feature and the extracted surface feature.

When a laser odometer factor is constructed, distortion removal correction processing can be performed on laser point cloud data firstly, specifically, rotation increment can be calculated by utilizing IMU data corresponding to the laser point cloud data of a current frame, translation increment is calculated by utilizing IMU pre-integration, motion distortion correction is performed on a laser point of the laser point cloud data at each moment, meanwhile, the pose of the laser point cloud data of the current frame is roughly initialized by utilizing the pose angle of the IMU data and the pose corresponding to the IMU pre-integration, and therefore distortion removal processing of the laser point cloud data is completed.

And then calculating the curvature of each point for the laser point cloud data after motion distortion correction, further extracting angular point features and surface features based on the curvature, and calculating the pose of the laser point cloud data according to the angular point features and the surface features so as to construct a laser odometer factor.

In some embodiments of the present application, before performing optimization based on a factor of the preset map optimization algorithm to obtain an optimized pose, the method further includes: performing loop detection according to the laser point cloud data; and constructing a loop factor according to a loop detection result, wherein the loop factor is used as a factor of the preset graph optimization algorithm.

When the laser SLAM mapping method and device are used for achieving laser SLAM mapping in a specific scene, optimization efficiency and optimization effect can be further improved by adding loop constraints, vehicles can be controlled to run according to loop tracks in the early stage, and the collected mapping data can be used for subsequent loop detection. The loop detection can be specifically carried out by utilizing the existing loop detection algorithm such as a bag-of-words model or a deep learning-based method, so that a pose constraint relation can be established based on a loop detection result, pose optimization is carried out by combining other factors, and the pose optimization efficiency and the pose optimization effect are improved.

In some embodiments of the present application, the mapping data further includes wheel speed data, and before optimizing based on a factor of the preset map optimization algorithm to obtain an optimized pose, the method further includes: determining position data corresponding to each frame of wheel speed data based on the wheel speed data; and constructing a wheel speed factor according to the position data corresponding to each frame of wheel speed data, wherein the wheel speed factor is used as a factor of the preset map optimization algorithm.

Under the specific map construction scenes such as a tunnel and the like, the wheel speed data acquired based on the wheel speed meter on the vehicle is less influenced by external factors and also has higher precision, so that the wheel speed factor can be further constructed based on the wheel speed data, and richer constraint information is provided for the subsequent pose optimization.

Specifically, under a map building scene, it can be considered that both the lateral speed and the radial speed in the wheel speed data acquired by the wheel speed meter are 0, that is, only the speed in the forward direction exists, and since the initial position of the vehicle before entering the preset positioning area is known, the position corresponding to each moment can be calculated based on the speed in the forward direction acquired by the wheel speed meter in real time, and the position constraint can be constructed based on the position data, so that more reference information is provided for the subsequent optimization process, and the optimization efficiency and the optimization effect are further improved.

In some embodiments of the present application, after performing optimization based on the factors of the preset map optimization algorithm to obtain an optimized pose, and performing map construction according to the optimized pose, the method further includes: comparing the optimized pose with the high-precision inertial navigation pose; determining the mapping precision 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 determines whether to rebuild the map according to the mapping accuracy of the optimized pose.

In order to evaluate the quality of the map construction, after the optimized pose is obtained, the optimized pose used for the map construction can be compared with the high-precision inertial navigation data, and due to the fact that the precision of the high-precision inertial navigation data is high, the pose data corresponding to the high-precision inertial navigation data can be used as a basis for judging the error size of the optimized pose.

The mapping accuracy of the optimized pose can be determined according to the comparison result of the pose and the pose, for example, if the horizontal angle error in the attitude angle is smaller than 0.1, the course angle error is smaller than 0.2, the corresponding statistical error is smaller than twice the standard deviation (2sigma), the position error is smaller than 0.15m, and the corresponding statistical error is smaller than twice the standard deviation (2sigma), the mapping accuracy is considered to be high, the mapping result can be scored in this way, or whether the mapping result meets the preset accuracy requirement or not is represented in other forms, the result is returned to the vehicle end, and the vehicle end can judge whether secondary mapping is needed or not according to the result. As shown in fig. 2, a schematic diagram of a laser SLAM mapping effect in a tunnel scene in the embodiment of the present application is provided.

In summary, the laser SLAM mapping method of the present application achieves at least the following technical effects:

1) the laser SLAM mapping under special scenes such as tunnels is realized based on the vehicle-cloud integrated framework, and the mapping efficiency is high;

2) the existing laser SLAM mapping scheme is optimized based on high-precision inertial navigation data, has higher mapping precision and better meets the mapping requirements in special scenes such as tunnels and the like;

3) and evaluating the mapping effect based on the high-precision inertial navigation data, ensuring the mapping precision and the mapping effect and providing powerful support for subsequent vehicle positioning.

The embodiment of the present application further provides a laser SLAM mapping apparatus 300, as shown in fig. 3, which provides a schematic structural diagram of the laser SLAM mapping apparatus in the embodiment of the present application, where the apparatus 300 at least includes: an obtaining unit 310, a post-processing unit 320, a first constructing unit 330, and an optimizing unit 340, wherein:

the acquisition unit 310 is configured to acquire mapping data acquired 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;

the post-processing unit 320 is 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;

the first constructing unit 330 is 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 odometer factor and a high-precision inertial navigation factor;

and the optimizing unit 340 is configured to optimize based on the factor of the preset map optimization algorithm to obtain an optimized pose, and construct a map according to the optimized pose.

In some embodiments of the present application, the post-processed high-precision inertial navigation data further includes IMU data, and the first constructing unit 330 is specifically configured to: performing IMU pre-integration processing based on the IMU data to obtain an IMU pre-integration factor as a factor of the preset map optimization algorithm; the optimization unit is specifically configured to: and optimizing based on the IMU pre-integration factor, the laser odometer factor and the high-precision inertial navigation factor to obtain an optimized pose.

In some embodiments of the present application, the optimization unit 340 is specifically configured to: adjusting pose confidence coefficients of the IMU pre-integration factor, the laser odometer factor and the high-precision inertial navigation factor based on the high-precision inertial navigation factor; and optimizing according to the pose confidence coefficient of the adjusted IMU pre-integration factor, the pose confidence coefficient of the adjusted laser odometer factor and the pose confidence coefficient of the adjusted high-precision inertial navigation factor to obtain the optimized pose.

In some embodiments of the present application, the post-processed high-precision inertial navigation data further includes IMU data, and the first constructing unit 330 is specifically configured to: carrying out distortion removal processing on the laser point cloud data by using the IMU data to obtain the laser point cloud data after distortion removal; extracting angular point features and surface features in the undistorted laser point cloud data; and constructing the laser odometry factor according to the extracted corner feature and the extracted surface feature.

In some embodiments of the present application, the apparatus further comprises: the loop detection unit is used for carrying out loop detection according to the laser point cloud data; and the second construction unit is used for constructing a loop factor according to a loop detection result, and the loop factor is used as a factor of the preset graph optimization algorithm.

In some embodiments of the present application, the apparatus further comprises: a first determination unit for determining position data corresponding to each frame of wheel speed data based on the wheel speed data; and the third construction unit is used for constructing a wheel speed factor according to the position data corresponding to each frame of wheel speed data, and the wheel speed factor is used as a factor of the preset map optimization algorithm.

In some embodiments of the present application, the apparatus further comprises: the comparison unit is used for comparing the optimized pose with the high-precision inertial navigation pose; the second determining unit is used for determining the mapping precision of the optimized pose according to the comparison result; and the returning unit is used for returning the mapping accuracy of the optimized pose to the vehicle end so that the vehicle end can determine whether to rebuild the map according to the mapping accuracy of the optimized pose.

It can be understood that the laser SLAM mapping apparatus can implement the steps of the laser SLAM mapping method provided in the foregoing embodiment, and the explanations related to the laser SLAM mapping method are applicable to the laser SLAM mapping apparatus, and are not repeated here.

Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application. Referring to fig. 4, at a hardware level, the electronic device includes a processor, and optionally further includes an internal bus, a network interface, and a memory. The Memory may include a Memory, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory, such as at least 1 disk Memory. Of course, the electronic device may also include hardware required for other services.

The processor, the network interface, and the memory may be connected to each other via an internal bus, which may be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 4, but that does not indicate only one bus or one type of bus.

And the memory is used for storing programs. In particular, the program may include program code comprising computer operating instructions. The memory may include both memory and non-volatile storage and provides instructions and data to the processor.

The processor reads a corresponding computer program from the nonvolatile memory into the memory and then runs the computer program to form the laser SLAM mapping device on the logic level. The processor is used for executing the program stored in the memory and is specifically used for executing the following operations:

The method performed by the laser SLAM mapping apparatus according to the embodiment shown in fig. 1 of the present application may be applied to or implemented by a processor. The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor.

The electronic device may further execute the method executed by the laser SLAM mapping apparatus in fig. 1, and implement the functions of the laser SLAM mapping apparatus in the embodiment shown in fig. 1, which are not described herein again in this embodiment of the present application.

An embodiment of the present application further provides a computer-readable storage medium storing one or more programs, where the one or more programs include instructions, which, when executed by an electronic device including multiple application programs, enable the electronic device to perform a method performed by the laser SLAM mapping apparatus in the embodiment shown in fig. 1, and are specifically configured to perform:

As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present invention has been described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

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

The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.

Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, 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 disc read only memory (CD-ROM), Digital Versatile Disks (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.

As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A laser SLAM mapping method, wherein the method comprises the following steps:

2. The method of claim 1, wherein the post-processed high precision inertial navigation data further comprises IMU data, and constructing factors of a preset map optimization algorithm from the post-processed high precision inertial navigation data and the laser point cloud data comprises:

3. The method of claim 2, wherein the optimizing based on the IMU pre-integration factor, the laser odometry factor, and the high-precision inertial navigation factor to obtain the optimized pose comprises:

and optimizing according to the pose confidence coefficient of the adjusted IMU pre-integration factor, the pose confidence coefficient of the adjusted laser odometer factor and the pose confidence coefficient of the adjusted high-precision inertial navigation factor to obtain the optimized pose.

4. The method of claim 1, wherein the post-processed high-precision inertial navigation data further comprises IMU data, and constructing factors of a preset map optimization algorithm according to the post-processed high-precision inertial navigation data and the laser point cloud data comprises:

carrying out distortion removal processing on the laser point cloud data by using the IMU data to obtain the laser point cloud data after distortion removal;

5. The method of claim 1, wherein before optimizing based on factors of the preset map optimization algorithm to obtain an optimized pose, the method further comprises:

performing loop detection according to the laser point cloud data;

6. The method of claim 1, wherein the mapping data further comprises wheel speed data, and the method further comprises, prior to optimizing based on factors of the preset map optimization algorithm to obtain an optimized pose:

7. The method of claim 1, wherein after optimizing based on factors of the pre-map optimization algorithm to obtain an optimized pose and performing mapping according to the optimized pose, the method further comprises:

comparing the optimized pose with the high-precision inertial navigation pose;

and returning the mapping accuracy of the optimized pose to the vehicle end, so that the vehicle end determines whether to rebuild the map according to the mapping accuracy of the optimized pose.

8. A laser SLAM mapping device, wherein the device comprises:

9. An electronic device, comprising:

a processor; and

a memory arranged to store computer executable instructions which, when executed, cause the processor to perform the method of any of claims 1 to 7.

10. A computer readable storage medium storing one or more programs which, when executed by an electronic device comprising a plurality of application programs, cause the electronic device to perform the method of any of claims 1-7.