Map construction method, device, electronic device and storage medium

Abstract

The present invention provides a map construction method, apparatus, electronic device, and storage medium. The method comprises: controlling an acquisition device to collect raw laser data of a target geographic area; obtaining the raw laser data and sensor data collected by the acquisition device; performing dimensionality reduction processing on the raw laser data to obtain reduced-dimensionality laser data; and utilizing a SLAM algorithm to fuse the reduced-dimensionality laser data and the sensor data to obtain map data corresponding to the target geographic area. The solution provided by the present invention reduces the dimensionality of the raw laser data and then fuses the raw laser data, the reduced-dimensionality laser data, and the sensor data to obtain map data corresponding to the target geographic area, thereby accelerating the construction of point cloud maps and improving the accuracy of raster maps.

Description

Technical Field

[0001] The present invention relates to the field of laser surveying and mapping technology, and in particular to a map construction method, device, electronic equipment and storage medium. Background Art

[0002] In recent years, intelligence has become a trend in social development, and various advanced intelligent technologies have emerged. In many fields, technicians need to build maps of unknown scenes, so the method of using laser to build high-precision maps has been widely adopted.

[0003] Existing laser mapping technology uses 3D lidar to collect scene laser data, and uses SLAM algorithm to process the laser data to generate point cloud maps or raster maps for target positioning and navigation.

[0004] However, this method requires processing too much 3D laser data, making it difficult to process in real time on embedded platforms. In addition, the generated grid map is easily disturbed by dynamic targets, affecting its accuracy. Summary of the Invention

[0005] The present invention provides a map construction method, device, electronic device and storage medium, which accelerate the construction speed of point cloud maps and improve the accuracy of grid maps.

[0006] In a first aspect, an embodiment of the present invention provides a map construction method, comprising:

[0007] Controlling the acquisition equipment to collect raw laser data of the target geographical area;

[0008] Obtaining raw laser data and sensor data collected by the acquisition device, wherein the sensor data is used to represent the operating status of the acquisition device when collecting data from the target geographical area;

[0009] Performing dimensionality reduction processing on the original laser data to obtain dimensionality-reduced laser data;

[0010] The SLAM algorithm is used to perform data fusion processing on the dimension-reduced laser data and the sensor data to obtain map data corresponding to the target geographical area.

[0011] In an optional embodiment, the raw laser data includes laser data obtained by scanning the target geographical area with a plurality of laser scan lines;

[0012] The performing dimensionality reduction processing on the original laser data to obtain dimensionality-reduced laser data includes:

[0013] According to the angles between each laser scanning line and the horizontal plane, the target laser scanning line with the smallest angle is selected, and the target laser data obtained by scanning the target laser scanning line is obtained;

[0014] The target laser data serves as the dimension-reduced laser data.

[0015] In an optional embodiment, the SLAM algorithm is used to perform data fusion processing on the dimension-reduced laser data and the sensor data to obtain map data corresponding to the target geographical area, including:

[0016] Using the 2D-SLAM algorithm, the dimension-reduced laser data and the sensor data are processed to obtain 2D trajectory data of the acquisition device;

[0017] According to the original laser data corresponding to the dimension-reduced laser data, the 2D trajectory data is subjected to a fusion process based on data dimensions to obtain 3D trajectory data;

[0018] The original laser data, 3D trajectory data and the sensor data are processed using a 3D-SLAM algorithm to obtain a point cloud map in the map data corresponding to the target geographic area.

[0019] In an optional embodiment, the 3D-SLAM algorithm is used to process the raw laser data, the 3D trajectory data, and the sensor data to obtain map data corresponding to the target geographic area, including:

[0020] According to the original laser data, the point cloud map is subjected to noise point cloud removal processing to obtain a point cloud map from which the noise point cloud has been removed.

[0021] In an optional embodiment, the 2D-SLAM algorithm is used to process the reduced-dimensional laser data and the sensor data to obtain 2D trajectory data of the acquisition device, including:

[0022] Determining the acquisition time of the collected reduced-dimensionality laser data;

[0023] Selecting sensor data corresponding to the acquisition time and obtaining an initial motion state of the acquisition device at the acquisition time;

[0024] The 2D-SLAM algorithm is used to process the initial motion state of the acquisition device at the acquisition time and the dimension-reduced laser data to calculate and obtain the 2D trajectory data of the acquisition device.

[0025] In an optional embodiment, the step of using a SLAM algorithm to perform data fusion processing on the dimension-reduced laser data and the sensor data to obtain map data corresponding to the target geographical area further includes:

[0026] Obtain a pre-stored two-dimensional plan view of a target geographic area;

[0027] The 2D-SLAM algorithm is used to determine a matching relationship between the dimension-reduced laser data, the sensor data, and the two-dimensional plane map, and obtain a grid map in the map data corresponding to the target geographic area.

[0028] In an optional embodiment, after obtaining the grid map in the map data corresponding to the target geographical area, the method further includes:

[0029] determining interference targets in a target geographic area based on the reduced-dimensionality laser data and the 3D trajectory data;

[0030] According to the correspondence between the dimension-reduced laser data and the grid map, interference targets in the grid map in the map data corresponding to the target geographical area are removed.

[0031] In a second aspect, an embodiment of the present invention provides a map construction device, comprising: a data acquisition module, a data preprocessing module, and a map construction module;

[0032] The data acquisition module is used to control the acquisition equipment to acquire the original laser data of the target geographical area;

[0033] The data preprocessing module is used to obtain the original laser data and sensor data collected by the collection device;

[0034] The data preprocessing module is further used to perform dimensionality reduction processing on the original laser data to obtain reduced-dimensionality laser data;

[0035] The map construction module is used to use the SLAM algorithm to perform data fusion processing on the dimension-reduced laser data and the sensor data to obtain map data corresponding to the target geographical area.

[0036] In a third aspect, an embodiment of the present invention provides an electronic device, including:

[0037] memory, processors and computer programs;

[0038] The computer program is stored in the memory and is configured to be executed by the processor to implement the map construction method according to any one of the first aspects.

[0039] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the map construction method described in any one of the first aspects.

[0040] The present invention provides a ground data processing and acquisition method, which controls an acquisition device to acquire raw laser data of a target geographic area; obtains the raw laser data and sensor data acquired by the acquisition device; performs dimensionality reduction processing on the raw laser data to obtain reduced-dimensional laser data; and utilizes a SLAM algorithm to fuse the reduced-dimensional laser data and the sensor data to obtain map data corresponding to the target geographic area. The solution provided by the present invention reduces the dimensionality of the raw laser data and then fuses the raw laser data, the reduced-dimensional laser data, and the sensor data to obtain map data corresponding to the target geographic area, thereby accelerating the construction of point cloud maps and improving the accuracy of raster maps.

[0041] It should be understood that the contents described in the above summary of the invention are not intended to limit the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0043] Figure 1 A schematic diagram of a scenario architecture on which the present disclosure is based;

[0044] Figure 2 A flowchart of a map construction method provided in an embodiment of the present disclosure;

[0045] Figure 3 A flowchart of another map construction method provided by an embodiment of the present disclosure;

[0046] Figure 4 A flowchart of another map construction method provided in an embodiment of the present disclosure

[0047] Figure 5 A schematic structural diagram of a map construction device provided in an embodiment of the present disclosure;

[0048] Figure 6 A schematic structural diagram of an electronic device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0049] Embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the drawings and embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of protection of the present invention.

[0050] When exploring and studying unknown scenes, building a scene map is very important and is the basis for positioning and navigation. Among various map construction methods, laser mapping is widely used due to its high accuracy.

[0051] Laser scene mapping includes 2D laser mapping and 3D laser mapping. 3D laser mapping is more precise than 2D laser mapping, and therefore is increasingly used. When constructing a map, a robot equipped with a 3D laser radar moves through the scene, collecting laser scan data of the scene using the 3D laser radar. This data is then processed using a 3D-SLAM algorithm to generate a point cloud map of the scene and the robot's motion trajectory. If navigation is required, a raster map is generated from the scene point cloud map.

[0052] However, this map construction method requires processing a large amount of three-dimensional laser scanning data, and the calculation process consumes a lot of resources and time, so it is difficult to apply to embedded systems. Moreover, it is difficult to eliminate the interference of dynamic targets when generating a raster map through a point cloud map, resulting in interference information in the generated raster map.

[0053] To address these issues, the inventors discovered that using a 3D-SLAM algorithm to process three-dimensional laser scanning data is very time-consuming. Instead, the three-dimensional laser scanning data can be first reduced in dimension to obtain two-dimensional laser scanning data. The two-dimensional motion trajectory is calculated using the two-dimensional laser scanning data and sensor data. The 3D-SLAM algorithm is then used to process the two-dimensional motion trajectory and the three-dimensional laser scanning data. This process does not require processing all of the three-dimensional laser scanning data to generate a point cloud map and the robot's three-dimensional motion trajectory, saving a significant amount of computing time. The robot's three-dimensional motion trajectory obtained using the 3D-SLAM algorithm undergoes point cloud optimization iterations, removing interference from dynamic targets. This can be used to remove interfering targets from a grid map, resulting in a more accurate grid map. This invention scheme constructs a point cloud map and a three-dimensional motion trajectory by reducing the dimensionality of the laser data to generate a two-dimensional motion trajectory. The three-dimensional motion trajectory then removes interference information from the grid map, accelerating the construction of the point cloud map while improving the accuracy of the grid map.

[0054] Figure 1 This is a schematic diagram of a scenario architecture based on which the present disclosure is based. Figure 1 As shown, a scenario architecture based on which the present disclosure is based may include a map construction device 1 and a robot 2. The map construction method provided in the embodiment of the present application may be executed by the map construction device 1 provided in the embodiment of the present application. The map construction device 1 provided in the embodiment of the present application may be part or all of the robot 2.

[0055] The map building device 1 is hardware or software that can interact with the robot 2 and can be used to execute the map building methods described in the following embodiments.

[0056] When the map construction device 1 is hardware, it includes a server with computing capabilities. When the map construction device 1 is software, it can be installed in an electronic device with computing capabilities, including but not limited to a portable computer and a desktop computer.

[0057] The map building device 1 can run on the robot 2 and provide map building services for the robot 2. The robot 2 uses the map building device 1 to obtain map information for positioning and navigation.

[0058] At the same time, the map building device 1 can also use the 3D laser scanning function and sensors of the robot 2 to obtain three-dimensional scanning data and sensor data of the scene.

[0059] Of course, in other usage scenarios, the robot 2 may send the three-dimensional scanning data and the sensor data to the map building device 1 , so that the map building device 1 builds a map in the manner shown below.

[0060] The following specific embodiments are used to describe the technical solution of the present invention in detail. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0061] Figure 2 A flowchart of a map construction method provided in an embodiment of the present disclosure is shown in FIG. Figure 2 As shown, the method of this embodiment may include:

[0062] S21, controlling the acquisition device to acquire raw laser data of the target geographical area;

[0063] In this embodiment, the acquisition device is controlled to enter the target geographical area, perform laser scanning on the target geographical area, and acquire laser data of the target geographical area as raw laser data.

[0064] Specifically, the acquisition device emits a laser scanning line to the target geographical area and collects laser data of the target geographical area based on the laser reflection principle, which can be used to construct a map of the target geographical area and calculate the movement trajectory of the acquisition device.

[0065] For example, the collection device is a mobile robot equipped with a 3D laser radar. The robot is controlled to enter the target geographical area, emit laser scanning lines to the surrounding environment, and collect the reflected laser.

[0066] S22, obtaining raw laser data and sensor data collected by the acquisition device, wherein the sensor data is used to represent the operating status of the acquisition device when collecting data of the target geographical area;

[0067] Continuing with the above robot as an example, the robot is equipped with various types of sensors to collect motion information to assist in map construction. The acquired sensor data may include: speed information, mileage information, direction information, etc.

[0068] S23, performing dimensionality reduction processing on the original laser data to obtain dimensionality-reduced laser data;

[0069] In this embodiment, the original laser data includes laser data obtained by scanning the target geographical area with multiple laser scanning lines, while the calculation of 2D trajectory data only requires scanning data of one laser scanning line. The original laser data can be sampled according to a preset dimensionality reduction method. In one possible implementation, the original laser data is subjected to dimensionality reduction processing to obtain reduced-dimensionality laser data, including: selecting a target laser scanning line with the smallest angle based on the angle between each laser scanning line and the horizontal plane, and obtaining target laser data obtained by scanning the target laser scanning line; the target laser data is used as the reduced-dimensionality laser data.

[0070] For example, the original laser data includes laser data obtained by scanning the target geographical area with six laser scanning lines, and the angles between the six laser scanning lines and the horizontal plane are 10°, 20°, 30°, 40°, 50° and 60°, respectively. The laser scanning lines with an angle of 10° with the horizontal plane are selected as target laser scanning lines to obtain target laser data obtained by scanning the target laser scanning lines.

[0071] S24: Using a SLAM algorithm, perform data fusion processing on the dimension-reduced laser data and the sensor data to obtain map data corresponding to the target geographic area.

[0072] In this embodiment, the SLAM algorithms used include 2D-SLAM algorithm and 3D-SLAM algorithm. The 2D-SLAM algorithm and 3D-SLAM algorithm are used to fuse the original laser data, reduced-dimensional laser data and sensor data to obtain map data corresponding to the target geographical area.

[0073] The solution provided by the embodiments of the present disclosure controls a collection device to collect raw laser data of a target geographic area; obtains the raw laser data collected by the collection device and sensor data; performs dimensionality reduction processing on the raw laser data to obtain reduced-dimensionality laser data; and utilizes a SLAM algorithm to perform data fusion processing on the reduced-dimensionality laser data and the sensor data to obtain map data corresponding to the target geographic area. The solution provided by the present invention reduces the dimensionality of the raw laser data and then fuses the raw laser data, the reduced-dimensionality laser data, and the sensor data to obtain map data corresponding to the target geographic area, thereby accelerating the construction of point cloud maps and improving the accuracy of raster maps.

[0074] The method uses the SLAM algorithm to perform data fusion processing on the reduced-dimensionality laser data and the sensor data to obtain map data corresponding to the target geographical area, including: using the 2D-SLAM algorithm to process the reduced-dimensionality laser data and the sensor data to obtain 2D trajectory data of the acquisition device; performing data dimension-based fusion processing on the 2D trajectory data based on the original laser data corresponding to the reduced-dimensionality laser data to obtain 3D trajectory data; and using the 3D-SLAM algorithm to process the original laser data, 3D trajectory data and the sensor data to obtain a point cloud map in the map data corresponding to the target geographical area.

[0075] Since it takes a lot of time to directly use the 3D-SLAM algorithm to process the original laser data to obtain 3D trajectory data and point cloud maps, the 2D-SLAM algorithm can be used to process the reduced-dimensional laser data to obtain the 2D trajectory data of the acquisition device. The 2D trajectory data of the acquisition device and the original laser data are fused to quickly obtain the 3D trajectory data. The 3D-SLAM algorithm is used to process the original laser data, 3D trajectory data and sensor data to quickly obtain the point cloud map in the map data corresponding to the target geographic area.

[0076] In an alternative embodiment, in the above Figure 2 Based on the above embodiments, Figure 3 Flowchart of another map construction method provided by the embodiment of the present disclosure. Figure 3 As shown, S24 specifically includes:

[0077] S241. Process the reduced-dimensionality laser data and the sensor data using the 2D-SLAM algorithm to obtain 2D trajectory data of an acquisition device.

[0078] In this embodiment, the initial motion state of the acquisition device is calculated using sensor data, and the 2D-SLAM algorithm can calculate the 2D trajectory data of the acquisition device based on the initial motion state of the acquisition device and the reduced-dimensionality laser data.

[0079] Specifically, in one possible implementation, the 2D-SLAM algorithm is used to process the reduced-dimensionality laser data and the sensor data to obtain the 2D trajectory data of the acquisition device, including: determining the acquisition time of the collected reduced-dimensionality laser data; selecting the sensor data corresponding to the acquisition time, and obtaining the initial motion state of the acquisition device at the acquisition time; using the 2D-SLAM algorithm, the initial motion state of the acquisition device at the acquisition time and the reduced-dimensionality laser data are processed to calculate the 2D trajectory data of the acquisition device.

[0080] For example, the collection time of the reduced-dimensionality laser data is from 9:0:10 to 9:0:30. The sensor data from 9:0:10 to 9:0:30 is selected to obtain the motion state of the acquisition device including speed, distance and direction. The 2D-SLAM algorithm is used to calculate the 2D trajectory data of the acquisition device based on the reduced-dimensionality laser data from 9:0:10 to 9:0:30 and the motion state of the acquisition device.

[0081] S242, performing data dimension-based fusion processing on the 2D trajectory data according to the original laser data corresponding to the dimension-reduced laser data to obtain 3D trajectory data;

[0082] In this embodiment, on the basis of obtaining the 2D trajectory data, the 2D trajectory data is further processed according to the original laser data corresponding to the dimension-reduced laser data, and the data of other dimensions are fused into the 2D trajectory data to obtain the 3D trajectory data.

[0083] For example, the 2D trajectory data from 9:0:10 to 9:0:30 was obtained, and the 2D trajectory data included three dimensions: x, y, and yaw. The original laser data corresponding to the dimensionality-reduced laser data from 9:0:10 to 9:0:30 was obtained. Based on the original laser data from 9:0:10 to 9:0:30, the three dimensions of z, roll, and pitch were added to the 2D trajectory data. The obtained 3D trajectory data included six dimensions: x, y, z, roll, pitch, and yaw.

[0084] S243. Using a 3D-SLAM algorithm, the original laser data, the 3D trajectory data, and the sensor data are processed to obtain a point cloud map in the map data corresponding to the target geographic area.

[0085] In this embodiment, the 3D-SLAM algorithm can generate a point cloud map in the map data corresponding to the target geographic area based on the original laser data, 3D trajectory data and sensor data.

[0086] Furthermore, S24 specifically includes:

[0087] S244 , performing noise removal processing on the point cloud map according to the original laser data to obtain a point cloud map from which noise has been removed.

[0088] In this embodiment, as the amount of original laser data increases, noise point clouds are determined through multiple point cloud iterations, and the noise point clouds in the map are removed.

[0089] Different from the aforementioned embodiments, the solution provided by the embodiments of the present disclosure utilizes a 2D-SLAM algorithm to process the reduced-dimensional laser data to obtain 2D trajectory data of the acquisition device, fuses the 2D trajectory data of the acquisition device with the original laser data to obtain 3D trajectory data, and utilizes a 3D-SLAM algorithm to process the original laser data, 3D trajectory data, and sensor data to obtain a point cloud map in the map data corresponding to the target geographic area, thereby significantly improving the processing speed.

[0090] Since navigation tasks may be performed during the map construction process, it is necessary to further generate a raster map.

[0091] In an alternative embodiment, in the above Figure 2 Based on the above embodiments, Figure 4 Flowchart of another map construction method provided by the embodiment of the present disclosure. Figure 4 As shown, S24 specifically includes:

[0092] S245, obtaining a pre-stored two-dimensional plan view of the target geographical area;

[0093] In this embodiment, a two-dimensional plan view of the target geographical area is obtained and used to correct the grid map, thereby improving the accuracy of the grid map.

[0094] It should be noted that the two-dimensional plan of the target geographical area may include a building floor plan, a regional floor plan, and the like.

[0095] S246. Using the 2D-SLAM algorithm, determine the matching relationship between the reduced-dimensionality laser data, the sensor data, and the two-dimensional plane map, and obtain a grid map in the map data corresponding to the target geographic area.

[0096] In this embodiment, the 2D-SLAM algorithm can generate a grid map in the map data corresponding to the target geographic area based on the reduced-dimensional laser data and the sensor data, and correct the grid map using the relevant information of the two-dimensional plane map based on the matching relationship with the two-dimensional plane map.

[0097] Since there may be interference targets in the target geographical area, the generated raster map also includes interference targets, so the interference targets need to be removed. In a possible implementation, after obtaining the raster map in the map data corresponding to the target geographical area, it also includes: determining the interference targets in the target geographical area based on the reduced-dimensionality laser data and the 3D trajectory data; and removing the interference targets in the raster map in the map data corresponding to the target geographical area based on the correspondence between the reduced-dimensionality laser data and the raster map.

[0098] The solution provided by the embodiment of the present disclosure uses the 2D-SLAM algorithm to generate a grid map, corrects the grid map using a pre-stored two-dimensional plan view of the target geographic area, and uses 3D trajectory data to remove interfering targets, thereby improving the accuracy of the grid map.

[0099] Figure 5 A structural diagram of a map construction device provided by an embodiment of the present disclosure is shown in FIG. Figure 5 As shown, the device of this embodiment may include:

[0100] The data acquisition module 51 is used to control the acquisition equipment to collect raw laser data of the target geographical area;

[0101] A data preprocessing module 52 is used to obtain the raw laser data and sensor data collected by the acquisition device;

[0102] The data preprocessing module 52 is further used to perform dimensionality reduction processing on the original laser data to obtain reduced-dimensionality laser data;

[0103] The map construction module 53 is used to use the SLAM algorithm to perform data fusion processing on the dimension-reduced laser data and the sensor data to obtain map data corresponding to the target geographical area.

[0104] Optionally, the data preprocessing module 32 is specifically used to select the target laser scanning line with the smallest angle based on the angle between each laser scanning line and the horizontal plane, and obtain target laser data obtained by scanning the target laser scanning line; the target laser data is used as the reduced-dimensional laser data.

[0105] Optionally, the map construction module 33 is specifically used to use the 2D-SLAM algorithm to process the reduced-dimensionality laser data and the sensor data to obtain 2D trajectory data of the acquisition device; perform data dimension-based fusion processing on the 2D trajectory data according to the original laser data corresponding to the reduced-dimensionality laser data to obtain 3D trajectory data; use the 3D-SLAM algorithm to process the original laser data, 3D trajectory data and the sensor data to obtain a point cloud map in the map data corresponding to the target geographical area; perform noise point cloud processing on the point cloud map according to the original laser data to obtain a point cloud map with the noise point cloud removed.

[0106] Optionally, the map construction module 33 is specifically used to determine the acquisition time of the collected reduced-dimensionality laser data; select the sensor data corresponding to the acquisition time, and obtain the initial motion state of the acquisition device at the acquisition time; use the 2D-SLAM algorithm to process the initial motion state of the acquisition device at the acquisition time and the reduced-dimensionality laser data, and calculate the 2D trajectory data of the acquisition device.

[0107] Optionally, the map construction module 33 is specifically used to obtain a pre-stored two-dimensional plan view of the target geographical area; using the 2D-SLAM algorithm, determine the matching relationship between the reduced-dimensional laser data, the sensor data and the two-dimensional plan view, and obtain a raster map in the map data corresponding to the target geographical area.

[0108] Optionally, the map construction module 33 is specifically used to determine the interference targets in the target geographical area based on the reduced-dimensionality laser data and the 3D trajectory data; and remove the interference targets in the grid map in the map data corresponding to the target geographical area based on the correspondence between the reduced-dimensionality laser data and the grid map.

[0109] The device of this embodiment can be used to execute the technical solution of the above method embodiment. Its implementation principle is similar and will not be repeated here.

[0110] The disclosed embodiments provide a map construction device that controls a collection device to collect raw laser data of a target geographic area; obtains the raw laser data collected by the collection device and sensor data; performs dimensionality reduction processing on the raw laser data to obtain reduced-dimensionality laser data; and utilizes a SLAM algorithm to perform data fusion processing on the reduced-dimensionality laser data and the sensor data to obtain map data corresponding to the target geographic area. The solution provided by the present invention reduces the dimensionality of the raw laser data and then fuses the raw laser data, the reduced-dimensionality laser data, and the sensor data to obtain map data corresponding to the target geographic area, thereby accelerating the construction of point cloud maps and improving the accuracy of raster maps.

[0111] Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of the present disclosure is shown in FIG. Figure 6 As shown, the electronic device 60 of this embodiment may include: a memory 61 and a processor 62.

[0112] Memory 61, used for storing computer programs (such as application programs, functional modules, etc. that implement the above-mentioned map construction method), computer instructions, etc.;

[0113] The aforementioned computer programs, computer instructions, etc. may be partitioned and stored in one or more memories 61 . Furthermore, the aforementioned computer programs, computer instructions, data, etc. may be called by the processor 62 .

[0114] The processor 62 is configured to execute the computer program stored in the memory 61 to implement the various steps in the method involved in the above embodiment.

[0115] For details, please refer to the relevant description in the previous method embodiment.

[0116] The memory 61 and the processor 62 may be independent structures or integrated structures. When the memory 61 and the processor 62 are independent structures, the memory 61 and the processor 62 may be coupled to each other via a bus 63 .

[0117] An electronic device of this embodiment can perform Figure 2 、 Figure 3 and Figure 4 The technical solution in the method shown, its specific implementation process and technical principles can be found in Figure 2 、 Figure 3 and Figure 4 The relevant descriptions in the method shown will not be repeated here.

[0118] In addition, an embodiment of the present application further provides a computer-readable storage medium, in which computer-executable instructions are stored. When at least one processor of a user device executes the computer-executable instructions, the user device executes the various possible methods described above.

[0119] Computer-readable media include computer storage media and communication media, wherein communication media include any medium that facilitates the transfer of computer programs from one location to another. The storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer. An exemplary storage medium is coupled to a processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be an integral part of the processor. The processor and storage medium can be located in an ASIC. Alternatively, the ASIC can be located in a user device. Of course, the processor and storage medium can also exist as discrete components in a communication device.

[0120] Those skilled in the art will appreciate that all or part of the steps in the above-described method embodiments can be implemented using hardware associated with program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0121] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A map construction method, characterized in that: include: Controlling an acquisition device to acquire raw laser data of a target geographical area, wherein the raw laser data includes laser data obtained by scanning the target geographical area with a plurality of laser scanning lines; Obtaining raw laser data and sensor data collected by the acquisition device, wherein the sensor data is used to represent the operating status of the acquisition device when collecting data from the target geographic area, the operating status including speed information, mileage information, and direction information; Performing dimensionality reduction processing on the original laser data to obtain dimensionality-reduced laser data; Using a SLAM algorithm, performing data fusion processing on the dimension-reduced laser data and the sensor data to obtain map data corresponding to the target geographic area; The performing dimensionality reduction processing on the original laser data to obtain dimensionality-reduced laser data includes: According to the angles between each laser scanning line and the horizontal plane, the target laser scanning line with the smallest angle is selected, and the target laser data obtained by scanning the target laser scanning line is obtained; The target laser data serves as the dimension-reduced laser data.

2. The method according to claim 1, characterized in that The SLAM algorithm is used to perform data fusion processing on the dimension-reduced laser data and the sensor data to obtain map data corresponding to the target geographical area, including: Using a 2D-SLAM algorithm, the dimension-reduced laser data and the sensor data are processed to obtain 2D trajectory data of the acquisition device; According to the original laser data corresponding to the dimension-reduced laser data, the 2D trajectory data is subjected to a fusion process based on data dimensions to obtain 3D trajectory data; The original laser data, 3D trajectory data and the sensor data are processed using a 3D-SLAM algorithm to obtain a point cloud map in the map data corresponding to the target geographic area.

3. The method according to claim 2, characterized in that The 3D-SLAM algorithm is used to process the original laser data, 3D trajectory data, and the sensor data to obtain map data corresponding to the target geographic area, including: According to the original laser data, the point cloud map is subjected to noise point cloud removal processing to obtain a point cloud map from which the noise point cloud has been removed.

4. The method according to claim 2, characterized in that The method of processing the reduced-dimensional laser data and the sensor data using the 2D-SLAM algorithm to obtain 2D trajectory data of the acquisition device includes: Determining the acquisition time of the collected reduced-dimensionality laser data; Selecting sensor data corresponding to the acquisition time and obtaining an initial motion state of the acquisition device at the acquisition time; The 2D-SLAM algorithm is used to process the initial motion state of the acquisition device at the acquisition time and the dimension-reduced laser data to calculate and obtain the 2D trajectory data of the acquisition device.

5. The method according to claim 2, characterized in that The method further comprises: performing data fusion processing on the dimension-reduced laser data and the sensor data by using a SLAM algorithm to obtain map data corresponding to the target geographical area; Obtain a pre-stored two-dimensional plan view of a target geographic area; The 2D-SLAM algorithm is used to determine a matching relationship between the dimension-reduced laser data, the sensor data, and the two-dimensional plane map, and obtain a grid map in the map data corresponding to the target geographic area.

6. The method according to claim 5, characterized in that After obtaining the grid map in the map data corresponding to the target geographical area, the method further includes: determining interference targets in a target geographic area based on the reduced-dimensionality laser data and the 3D trajectory data; According to the correspondence between the dimension-reduced laser data and the grid map, interference targets in the grid map in the map data corresponding to the target geographical area are removed.

7. A map construction device, characterized in that: include: Data acquisition module, data preprocessing module and map construction module; The data acquisition module is used to control the acquisition device to acquire raw laser data of the target geographical area, wherein the raw laser data includes laser data obtained by scanning the target geographical area with multiple laser scanning lines; The data preprocessing module is used to obtain raw laser data and sensor data collected by the acquisition device, wherein the sensor data is used to represent the operating status of the acquisition device when collecting data in the target geographical area, and the operating status includes speed information, mileage information, and direction information; The data preprocessing module is further used to perform dimensionality reduction processing on the original laser data to obtain reduced-dimensionality laser data; The map construction module is used to perform data fusion processing on the dimension-reduced laser data and the sensor data using a SLAM algorithm to obtain map data corresponding to the target geographical area; The data preprocessing module is specifically used to select the target laser scanning line with the smallest angle based on the angle between each laser scanning line and the horizontal plane, and obtain the target laser data obtained by scanning the target laser scanning line; the target laser data is used as the dimensionality reduction laser data.

8. An electronic device, characterized in that: include: at least one processor and memory; The memory stores computer-executable instructions; The at least one processor executes the computer-executable instructions stored in the memory, so that the at least one processor performs the map construction method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions. When a processor executes the computer-executable instructions, the map construction method according to any one of claims 1 to 6 is implemented.

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