Map construction optimization method and device, electronic equipment and readable storage medium

Abstract

This application discloses a map building optimization method, apparatus, electronic device, and readable storage medium, applied in the field of data processing technology. The map building optimization method includes: acquiring at least two unit maps generated by a preset mapping algorithm based on laser data; performing edge optimization on each unit map to obtain an optimized unit map; processing each optimized unit map based on the overlapping areas between each pair of optimized unit maps to obtain a processed unit map; and combining the processed unit maps into a target map. This application solves the technical problem of inaccurate map building.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a map building optimization method, apparatus, electronic device, and readable storage medium. Background Technology

[0002] Currently, maps are typically constructed using LiDAR data and pre-defined mapping algorithms. Specifically, at least two unit maps are built based on the LiDAR data, and a target map is constructed based on the LiDAR data and the poses corresponding to each unit map. However, because LiDAR data may be unstable and have low resolution, the constructed target map may contain jagged edges, ghosting, or non-straight lines, leading to inaccurate map construction. Summary of the Invention

[0003] The main objective of this application is to provide a map building optimization method, apparatus, electronic device, and readable storage medium, aiming to solve the technical problem of inaccurate map building in the prior art.

[0004] To achieve the above objectives, this application provides a map construction optimization method, which includes:

[0005] Obtain at least two unit maps generated by a preset mapping algorithm based on laser data;

[0006] Edge optimization is performed on each of the aforementioned unit graphs to obtain an optimized unit graph;

[0007] Based on the overlapping areas between each pair of the optimized unit maps, each optimized unit map is processed to obtain a processed unit map, and the processed unit maps are combined into a target map.

[0008] Optionally, before the step of obtaining at least two unit maps generated by the preset mapping algorithm based on the laser data, the method further includes:

[0009] The laser data is subjected to noise removal to obtain processed laser data;

[0010] The processed laser data is subjected to linear fitting to obtain the fitted line points;

[0011] The processed laser data is replaced according to the coordinates corresponding to the fitted straight line points to obtain target laser data, which is then used by the preset mapping algorithm to generate at least two unit maps based on the target laser data.

[0012] Optionally, the step of performing edge optimization on each of the unit graphs to obtain an optimized unit graph includes:

[0013] Each of the aforementioned unit images is enlarged by a preset factor to obtain an enlarged unit image;

[0014] Obtain the laser origin, and connect the laser origin with each of the fitted line points in each magnified unit image to obtain the connected region;

[0015] Laser data within the connected area is cleared to perform edge optimization on each of the magnification unit images. Each of the magnification unit images is then reduced by the preset factor to obtain the optimized unit image.

[0016] Optionally, the step of processing each of the optimized unit graphs to obtain a processed unit graph based on the overlapping areas between each pair of the optimized unit graphs includes:

[0017] Select the first unit diagram group from each of the aforementioned optimization unit diagrams;

[0018] Based on the overlapping areas between each pair of optimized unit graphs in the first unit graph group, the first unit graph group is processed to obtain the first processed unit graph group.

[0019] In each of the optimization unit graphs, select a second unit graph that is adjacent to the first processing unit graph group;

[0020] Based on the first processing unit graph group, the second unit graph is processed to obtain a second processing unit graph, and the second processing unit graph is added to the first processing unit graph group.

[0021] Return to the step of selecting a second unit graph adjacent to the first processing unit graph group in each of the optimized unit graphs, and proceed with subsequent steps until all of the optimized unit graphs have been selected.

[0022] Optionally, the step of selecting the first unit map group in each of the optimized unit maps includes:

[0023] Obtain the relative position information of each of the optimized unit graphs in the global map constructed by the preset mapping algorithm;

[0024] In each of the optimized unit diagrams, select the first unit diagram group whose relative position information satisfies the preset relative position conditions.

[0025] Optionally, the first unit graph group includes a first unit subgraph and a second unit subgraph, and the first processing unit graph group includes a first processing unit subgraph and a second processing unit subgraph.

[0026] The step of processing the first unit graph group based on the overlapping areas between each pair of optimized unit graphs in the first unit graph group to obtain the first processed unit graph group includes:

[0027] The first unit subgraph is converted into first point cloud data, and the second unit subgraph is converted into second point cloud data;

[0028] Obtain the third point cloud data corresponding to the first unit subgraph and the fourth point cloud data corresponding to the second unit subgraph within the overlapping area between the first unit subgraph and the second unit subgraph;

[0029] Based on the first transformation relationship between the third point cloud data and the fourth point cloud data, and the second transformation relationship between the first unit sub-graph and the global map constructed by the preset mapping algorithm, the third transformation relationship between the second unit sub-graph and the global map is determined.

[0030] According to the second transformation relationship, the first point cloud data is transformed into the fifth point cloud data to obtain the first processing unit subgraph, and according to the third transformation relationship, the second point cloud data is transformed into the sixth point cloud data to obtain the second processing unit subgraph.

[0031] Optionally, the step of combining the processed unit maps into a target map includes:

[0032] The processing unit maps are clustered and combined to obtain a cluster map;

[0033] The clustered map is then converted into a two-dimensional map to obtain the target map.

[0034] To achieve the above objectives, this application also provides a map building optimization apparatus, the map building optimization apparatus comprising:

[0035] The acquisition module is used to acquire at least two unit maps generated by a preset mapping algorithm based on laser data;

[0036] The optimization module is used to perform edge optimization on each of the unit graphs to obtain an optimized unit graph;

[0037] The processing module is used to process each of the optimization unit maps according to the overlapping areas between each pair of optimization unit maps to obtain a processed unit map, and to combine the processed unit maps into a target map.

[0038] Optionally, before the step of obtaining at least two unit maps generated by the preset mapping algorithm based on the laser data, the map building optimization device is further configured to:

[0039] The laser data is subjected to noise removal to obtain processed laser data;

[0040] The processed laser data is subjected to linear fitting to obtain the fitted line points;

[0041] The processed laser data is replaced according to the coordinates corresponding to the fitted straight line points to obtain target laser data, which is then used by the preset mapping algorithm to generate at least two unit maps based on the target laser data.

[0042] Optionally, the optimization module is further configured to:

[0043] Each of the aforementioned unit images is enlarged by a preset factor to obtain an enlarged unit image;

[0044] Obtain the laser origin, and connect the laser origin with each of the fitted line points in each magnified unit image to obtain the connected region;

[0045] Laser data within the connected area is cleared to perform edge optimization on each of the magnification unit images. Each of the magnification unit images is then reduced by the preset factor to obtain the optimized unit image.

[0046] Optionally, the processing module is further configured to:

[0047] Select the first unit diagram group from each of the aforementioned optimization unit diagrams;

[0048] Based on the overlapping areas between each pair of optimized unit graphs in the first unit graph group, the first unit graph group is processed to obtain the first processed unit graph group.

[0049] In each of the optimization unit graphs, select a second unit graph that is adjacent to the first processing unit graph group;

[0050] Based on the first processing unit graph group, the second unit graph is processed to obtain a second processing unit graph, and the second processing unit graph is added to the first processing unit graph group.

[0051] Return to the step of selecting a second unit graph adjacent to the first processing unit graph group in each of the optimized unit graphs, and proceed with subsequent steps until all of the optimized unit graphs have been selected.

[0052] Optionally, the processing module is further configured to:

[0053] Obtain the relative position information of each of the optimized unit graphs in the global map constructed by the preset mapping algorithm;

[0054] In each of the optimized unit diagrams, select the first unit diagram group whose relative position information satisfies the preset relative position conditions.

[0055] Optionally, the first unit graph group includes a first unit subgraph and a second unit subgraph, the first processing unit graph group includes a first processing unit subgraph and a second processing unit subgraph, and the processing module is further configured to:

[0056] The first unit subgraph is converted into first point cloud data, and the second unit subgraph is converted into second point cloud data;

[0057] Obtain the third point cloud data corresponding to the first unit subgraph and the fourth point cloud data corresponding to the second unit subgraph within the overlapping area between the first unit subgraph and the second unit subgraph;

[0058] Based on the first transformation relationship between the third point cloud data and the fourth point cloud data, and the second transformation relationship between the first unit sub-graph and the global map constructed by the preset mapping algorithm, the third transformation relationship between the second unit sub-graph and the global map is determined.

[0059] According to the second transformation relationship, the first point cloud data is transformed into the fifth point cloud data to obtain the first processing unit subgraph, and according to the third transformation relationship, the second point cloud data is transformed into the sixth point cloud data to obtain the second processing unit subgraph.

[0060] Optionally, the processing module is further configured to:

[0061] The processing unit maps are clustered and combined to obtain a cluster map;

[0062] The clustered map is then converted into a two-dimensional map to obtain the target map.

[0063] This application also provides an electronic device, the electronic device comprising: a memory, a processor, and a program of the map building optimization method stored in the memory and executable on the processor, wherein when the program of the map building optimization method is executed by the processor, it can implement the steps of the map building optimization method as described above.

[0064] This application also provides a computer-readable storage medium storing a program that implements a map building optimization method. When the program is executed by a processor, it implements the steps of the map building optimization method as described above.

[0065] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the map building optimization method described above.

[0066] This application provides a map building optimization method, apparatus, electronic device, and readable storage medium. Compared to building maps using LiDAR data and a preset mapping algorithm, this application obtains at least two unit maps generated by the preset mapping algorithm based on LiDAR data; performs edge optimization on each unit map to obtain an optimized unit map; processes each optimized unit map based on the overlapping areas between each pair of optimized unit maps to obtain a processed unit map; and combines the processed unit maps into a target map. By performing edge optimization on the unit maps and processing the overlap between each pair of optimized unit maps, dual optimization of the unit maps can be achieved. This avoids the technical defects of the target map, which may have jagged edges, ghosting, or non-straight lines due to the instability and low resolution of LiDAR data, thereby improving the accuracy of map building. Attached Figure Description

[0067] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0068] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0069] Figure 1 This is a flowchart illustrating the first embodiment of the map construction optimization method of this application;

[0070] Figure 2 This is a schematic diagram of the device structure involved in the map construction optimization method in the embodiments of this application;

[0071] Figure 3 This is a schematic diagram of the device structure of the hardware operating environment involved in the map construction optimization method in this application embodiment.

[0072] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0073] To make the above-mentioned objectives, features, and advantages of this application more apparent and understandable, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0074] Example 1

[0075] This application provides a map building optimization method. In the first embodiment of the map building optimization method of this application, refer to... Figure 1 The map building optimization method includes:

[0076] Step S10: Obtain at least two unit maps generated by the preset mapping algorithm based on the laser data;

[0077] Step S20: Perform edge optimization on each of the unit graphs to obtain an optimized unit graph;

[0078] Step S30: Based on the overlapping areas between each pair of optimization unit maps, process each optimization unit map to obtain a processed unit map, and combine each processed unit map into a target map.

[0079] In this embodiment, it should be noted that the preset mapping algorithm is a pre-set algorithm for generating unit maps based on lidar data.

[0080] For example, steps S10 to S30 include: emitting laser data through a sensor, generating at least two unit maps based on the laser data using a preset mapping algorithm, and obtaining each unit map; performing edge optimization on each unit map to obtain an optimized unit map; processing each optimized unit map based on the overlapping areas between each pair of optimized unit maps to obtain a processed unit map, and combining each processed unit map into a target map.

[0081] In step S10, prior to the step of obtaining at least two unit maps generated by the preset mapping algorithm based on the laser data, the method further includes:

[0082] Step A10: Remove noise from the laser data to obtain processed laser data;

[0083] Step A20: Perform linear fitting on the processed laser data to obtain the fitted line points;

[0084] Step A30: Replace the processed laser data according to the coordinates corresponding to the fitted straight line points to obtain target laser data, so that the preset mapping algorithm can generate at least two unit maps based on the target laser data.

[0085] It is easy to understand that when laser data is directly input into a preset mapping algorithm to generate at least two unit maps based on the laser data, burrs may easily appear on the edges of the generated unit maps.

[0086] To overcome the above-mentioned defects, steps A10 to A30 include, for example, removing noise from the laser data to obtain processed laser data; performing line fitting on the processed laser data to obtain fitted line points; obtaining the coordinates corresponding to each fitted line point; and partially replacing the processed laser data according to the coordinates to obtain target laser data, so that the preset mapping algorithm can generate at least two unit maps based on the target laser data.

[0087] As an example, step A10 includes: obtaining a preset filtering algorithm, wherein the preset filtering algorithm can be a distance-based filtering algorithm, or other filtering algorithms such as an Euclidean distance filtering algorithm based on radius outlier search, and performing noise filtering on the laser data according to the preset filtering algorithm to obtain processed laser data.

[0088] By filtering noise and replacing straight lines in the laser data, a pre-set mapping algorithm generates unit maps based on the processed laser data. This results in unit maps with straighter edges, thereby improving the accuracy of map construction.

[0089] In step S20, the step of performing edge optimization on each of the unit graphs to obtain an optimized unit graph includes:

[0090] Step S21: Enlarge each of the unit images by a preset factor to obtain an enlarged unit image;

[0091] Step S22: Obtain the laser origin, and connect the laser origin with each of the fitted straight line points in each of the magnified unit diagrams to obtain the connected area;

[0092] Step S23: Clear the laser data in the connected area to perform edge optimization on each of the magnification unit images, and reduce each of the magnification unit images by the preset factor to obtain the optimized unit image.

[0093] In this embodiment, it should be noted that the preset magnification factor is a pre-set empirical magnification value.

[0094] For example, steps S21 to S23 include: obtaining a preset magnification factor, enlarging each of the unit maps by the preset magnification factor to obtain an enlarged unit map; obtaining the sensor coordinate origin corresponding to the sensor emitting laser data to obtain the laser origin point, connecting the laser origin point with each of the fitted straight line points in each of the enlarged unit maps to obtain a connected region; clearing the laser data in the connected region to perform edge optimization on each of the enlarged unit maps; and reducing each of the enlarged unit maps by the preset magnification factor to obtain the optimized unit map. By enlarging each unit map, the resolution of each unit map is improved, thereby clearing obstacle points under high-resolution images and improving the accuracy of map construction.

[0095] This application provides a map construction optimization method. Compared to constructing a map using LiDAR data and a preset mapping algorithm, this application obtains at least two unit maps generated by the preset mapping algorithm based on LiDAR data; performs edge optimization on each unit map to obtain an optimized unit map; processes each optimized unit map based on the overlapping areas between each pair of optimized unit maps to obtain a processed unit map; and combines the processed unit maps into a target map. By performing edge optimization on the unit maps and processing the overlap between each pair of optimized unit maps, dual optimization of the unit maps can be achieved. This avoids the technical defects of the target map, which may have jagged edges, ghosting, or non-straight lines due to the instability and low resolution of LiDAR data, thereby improving the accuracy of map construction.

[0096] Example 2

[0097] Furthermore, based on the first embodiment of this application, in another embodiment of this application, the content that is the same as or similar to that in the first embodiment can be referred to the above description and will not be repeated hereafter. Based on this, in step S30, the step of processing each of the optimization unit maps according to the overlapping areas between each pair of optimization unit maps to obtain a processed unit map includes:

[0098] Step S31: Select the first unit map group from each of the optimized unit maps;

[0099] Step S32: Based on the overlapping areas between each pair of optimized unit graphs in the first unit graph group, process the first unit graph group to obtain the first processed unit graph group;

[0100] Step S33: Select a second unit map that is adjacent to the first processing unit map group from each of the optimization unit maps;

[0101] Step S34: Based on the first processing unit graph group, process the second unit graph to obtain a second processing unit graph, and add the second processing unit graph to the first processing unit graph group;

[0102] Step S35: Return to the step of selecting a second unit graph adjacent to the first processing unit graph group in each of the optimized unit graphs and subsequent steps, until all of the optimized unit graphs have been selected.

[0103] In this embodiment, it should be noted that the second unit diagram can be single or multiple.

[0104] For example, steps S31 to S35 include: filtering a first unit map group in each of the optimization unit maps; processing the first unit map group according to the overlapping areas between each pair of optimization unit maps in the first unit map group to obtain a first processed unit map group; selecting a second unit map in each of the optimization unit maps that is other than the first unit map group and adjacent to the first processed unit map group; processing the second unit map according to the first processed unit map group adjacent to the second unit map to obtain a second processed unit map, and adding the second processed unit map to the first processed unit map group; returning to the step of selecting the second unit map adjacent to the first processed unit map group in each of the optimization unit maps and subsequent steps, until all optimization unit maps have been selected.

[0105] In step S31, the step of selecting the first unit map group in each of the optimized unit maps includes:

[0106] Step B10: Obtain the relative position information of each optimized unit graph in the global map constructed by the preset mapping algorithm;

[0107] Step B20: Select the first unit map group in each of the optimized unit maps whose relative position information satisfies the preset relative position conditions.

[0108] In this embodiment, it should be noted that the global map is a complete map composed of unit maps generated by a preset mapping algorithm. The preset relative position condition is a pre-set constraint on the relative position of the optimized unit map within the global map.

[0109] Understandably, the first unit map group is usually selected as the optimized unit map in the middle of the global map to avoid the situation where the map group processing efficiency is low when selecting edge positions because there may be no adjacent optimized unit map in a certain direction.

[0110] As an example, the relative position information includes relative position coordinates, and the preset relative position conditions include a preset range of relative position coordinates. Steps B10 to B20 include: obtaining the relative position coordinates of each of the optimized unit maps in the global map constructed by the preset mapping algorithm; and selecting a first unit map group in each of the optimized unit maps whose relative position coordinates satisfy the preset range of relative position coordinates.

[0111] As an example, the relative position information includes relative orientation and relative distance, and the preset relative position conditions include preset distance and preset orientation. Steps B10 to B20 include: obtaining the relative orientation and relative distance of each of the optimized unit maps to the center of the global map constructed by the preset mapping algorithm; and selecting a first unit map group in each of the optimized unit maps where the relative orientation satisfies the preset orientation and the relative distance satisfies the preset distance.

[0112] In step S32, the first unit graph group includes a first unit subgraph and a second unit subgraph, and the first processing unit graph group includes a first processing unit subgraph and a second processing unit subgraph.

[0113] The step of processing the first unit graph group based on the overlapping areas between each pair of optimized unit graphs in the first unit graph group to obtain the first processed unit graph group includes:

[0114] Step C10: Convert the first unit subgraph into first point cloud data, and convert the second unit subgraph into second point cloud data;

[0115] Step C20: Obtain the third point cloud data corresponding to the first unit subgraph and the fourth point cloud data corresponding to the second unit subgraph within the overlapping area between the first unit subgraph and the second unit subgraph.

[0116] Step C30: Based on the first transformation relationship between the third point cloud data and the fourth point cloud data and the second transformation relationship between the first unit sub-graph and the global map constructed by the preset mapping algorithm, determine the third transformation relationship between the second unit sub-graph and the global map.

[0117] Step C40: According to the second transformation relationship, the first point cloud data is transformed into the fifth point cloud data to obtain the first processing unit subgraph; and according to the third transformation relationship, the second point cloud data is transformed into the sixth point cloud data to obtain the second processing unit subgraph.

[0118] As an example, step C10 includes: assigning values ​​to the two-dimensional coordinate data corresponding to the first unit sub-graph in a three-dimensional format to obtain the first point cloud data, and assigning values ​​to the two-dimensional coordinate data corresponding to the second unit sub-graph in a three-dimensional format to obtain the second point cloud data.

[0119] As an example, steps C20 to C40 include: acquiring third point cloud data corresponding to the first unit sub-graph and fourth point cloud data corresponding to the second unit sub-graph within the overlapping area between the first unit sub-graph and the second unit sub-graph; performing overlap matching on the third point cloud data and the fourth point cloud data to obtain a first transformation relationship between the third point cloud data and the fourth point cloud data; determining a second transformation relationship between the first unit sub-graph and the global map based on the relative position information of the first unit sub-graph in the global map constructed by the preset mapping algorithm; determining a third transformation relationship between the second unit sub-graph and the global map based on the first transformation relationship and the second transformation relationship; converting the first point cloud data into fifth point cloud data according to the second transformation relationship to obtain a first processing unit sub-graph; and converting the second point cloud data into sixth point cloud data according to the third transformation relationship to obtain a second processing unit sub-graph.

[0120] In step S30, the step of combining the processed unit maps into a target map includes:

[0121] Step D10: Cluster and combine the graphs of each processing unit to obtain a cluster map;

[0122] Step D20: Perform two-dimensional processing on the clustered map to obtain the target map.

[0123] For example, steps D10 to D20 include: clustering and combining the processing unit maps according to their relative position information on the global map to obtain a clustered map; and performing two-dimensional processing on the clustered map to obtain a target map.

[0124] This application provides a map construction optimization method. Compared to constructing a map using LiDAR data and a preset mapping algorithm, this application obtains at least two unit maps generated by the preset mapping algorithm based on LiDAR data; performs edge optimization on each unit map to obtain an optimized unit map; processes each optimized unit map based on the overlapping areas between each pair of optimized unit maps to obtain a processed unit map; and combines the processed unit maps into a target map. By performing edge optimization on the unit maps and processing the overlap between each pair of optimized unit maps, dual optimization of the unit maps can be achieved. This avoids the technical defects of the target map, which may have jagged edges, ghosting, or non-straight lines due to the instability and low resolution of LiDAR data, thereby improving the accuracy of map construction.

[0125] Example 3

[0126] This application also provides a map building optimization device, referring to... Figure 2 The map building optimization device includes:

[0127] The acquisition module is used to acquire at least two unit maps generated by a preset mapping algorithm based on laser data;

[0128] The optimization module is used to perform edge optimization on each of the unit graphs to obtain an optimized unit graph;

[0129] The processing module is used to process each of the optimization unit maps according to the overlapping areas between each pair of optimization unit maps to obtain a processed unit map, and to combine the processed unit maps into a target map.

[0130] Optionally, before the step of obtaining at least two unit maps generated by the preset mapping algorithm based on the laser data, the map building optimization device is further configured to:

[0131] The laser data is subjected to noise removal to obtain processed laser data;

[0132] The processed laser data is subjected to linear fitting to obtain the fitted line points;

[0133] The processed laser data is replaced according to the coordinates corresponding to the fitted straight line points to obtain target laser data, which is then used by the preset mapping algorithm to generate at least two unit maps based on the target laser data.

[0134] Optionally, the optimization module is further configured to:

[0135] Each of the aforementioned unit images is enlarged by a preset factor to obtain an enlarged unit image;

[0136] Obtain the laser origin, and connect the laser origin with each of the fitted line points in each magnified unit image to obtain the connected region;

[0137] Laser data within the connected area is cleared to perform edge optimization on each of the magnification unit images. Each of the magnification unit images is then reduced by the preset factor to obtain the optimized unit image.

[0138] Optionally, the processing module is further configured to:

[0139] Select the first unit diagram group from each of the aforementioned optimization unit diagrams;

[0140] Based on the overlapping areas between each pair of optimized unit graphs in the first unit graph group, the first unit graph group is processed to obtain the first processed unit graph group.

[0141] In each of the optimization unit graphs, select a second unit graph that is adjacent to the first processing unit graph group;

[0142] Based on the first processing unit graph group, the second unit graph is processed to obtain a second processing unit graph, and the second processing unit graph is added to the first processing unit graph group.

[0143] Return to the step of selecting a second unit graph adjacent to the first processing unit graph group in each of the optimized unit graphs, and proceed with subsequent steps until all of the optimized unit graphs have been selected.

[0144] Optionally, the processing module is further configured to:

[0145] Obtain the relative position information of each of the optimized unit graphs in the global map constructed by the preset mapping algorithm;

[0146] In each of the optimized unit diagrams, select the first unit diagram group whose relative position information satisfies the preset relative position conditions.

[0147] Optionally, the first unit graph group includes a first unit subgraph and a second unit subgraph, the first processing unit graph group includes a first processing unit subgraph and a second processing unit subgraph, and the processing module is further configured to:

[0148] The first unit subgraph is converted into first point cloud data, and the second unit subgraph is converted into second point cloud data;

[0149] Obtain the third point cloud data corresponding to the first unit subgraph and the fourth point cloud data corresponding to the second unit subgraph within the overlapping area between the first unit subgraph and the second unit subgraph;

[0150] Based on the first transformation relationship between the third point cloud data and the fourth point cloud data, and the second transformation relationship between the first unit sub-graph and the global map constructed by the preset mapping algorithm, the third transformation relationship between the second unit sub-graph and the global map is determined.

[0151] According to the second transformation relationship, the first point cloud data is transformed into the fifth point cloud data to obtain the first processing unit subgraph, and according to the third transformation relationship, the second point cloud data is transformed into the sixth point cloud data to obtain the second processing unit subgraph.

[0152] Optionally, the processing module is further configured to:

[0153] The processing unit maps are clustered and combined to obtain a cluster map;

[0154] The clustered map is then converted into a two-dimensional map to obtain the target map.

[0155] The map building optimization apparatus provided in this application, employing the map building optimization method in the above embodiments, solves the technical problem of inaccurate map building. Compared with the prior art, the beneficial effects of the map building optimization apparatus provided in this application are the same as those of the map building optimization method provided in the above embodiments, and other technical features in this map building optimization apparatus are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0156] Example 4

[0157] This application provides an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the map building optimization method described in the above embodiments.

[0158] The following is for reference. Figure 3 The diagram illustrates a structural schematic of an electronic device suitable for implementing embodiments of the present disclosure. The electronic devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 3 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein.

[0159] like Figure 3As shown, an electronic device may include a processing unit (such as a central processing unit, graphics processing unit, etc.) that can perform various appropriate actions and processes based on a program stored in read-only memory (ROM) or a program loaded from a storage device into random access memory (RAM). The RAM also stores various programs and data required for the operation of the electronic device. The processing unit, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0160] Typically, the following systems can be connected to the I / O interface: input devices including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices including, for example, magnetic tapes, hard disks, etc.; and communication devices. Communication devices allow electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although electronic devices with various systems are shown in the figures, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems may be implemented alternatively.

[0161] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device, or installed from a ROM. When the computer program is executed by a processing device, it performs the functions defined above in the methods of embodiments of this disclosure.

[0162] The electronic device provided in this application employs the map building optimization method in the above embodiments, solving the technical problem of inaccurate map building. Compared with the prior art, the beneficial effects of the electronic device provided in this application are the same as those of the map building optimization method provided in the above embodiments, and other technical features of the electronic device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0163] It should be understood that various parts of this disclosure can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics may be combined in any suitable manner in one or more embodiments or examples.

[0164] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0165] Example 5

[0166] This embodiment provides a computer-readable storage medium having computer-readable program instructions stored thereon, which are used to execute the map construction optimization method in the above embodiment.

[0167] The computer-readable storage medium provided in this application embodiment may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination thereof.

[0168] The aforementioned computer-readable storage medium may be included in an electronic device or may exist independently without being assembled into an electronic device.

[0169] The aforementioned computer-readable storage medium carries one or more programs that, when executed by an electronic device, cause the electronic device to: acquire at least two unit maps generated by a preset mapping algorithm based on laser data; perform edge optimization on each of the unit maps to obtain an optimized unit map; process each of the optimized unit maps based on the overlapping areas between each pair of optimized unit maps to obtain a processed unit map; and combine the processed unit maps into a target map.

[0170] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smallport, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0171] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0172] The modules described in the embodiments of this disclosure can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0173] The computer-readable storage medium provided in this application stores computer-readable program instructions for executing the above-described map building optimization method, thus solving the technical problem of inaccurate map building. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the map building optimization method provided in the above-described embodiments, and will not be repeated here.

[0174] Example 6

[0175] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the map building optimization method described above.

[0176] The computer program product provided in this application solves the technical problem of inaccurate map construction. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as the beneficial effects of the map construction optimization method provided in the above embodiments, and will not be repeated here.

[0177] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent scope of this application.

Claims

1. A map construction optimization method, characterized in that, The map construction optimization method includes: Noise removal is performed on the laser data to obtain processed laser data; The processed laser data is subjected to linear fitting to obtain the fitted line points; The processed laser data is replaced based on the coordinates corresponding to the fitted straight line points to obtain the target laser data; Obtain at least two unit maps generated by a preset mapping algorithm based on laser data; Each of the aforementioned unit images is enlarged by a preset factor to obtain an enlarged unit image; Obtain the laser origin, and connect the laser origin with each of the fitted line points in each magnified unit image to obtain the connected region; The laser data in the connected area is cleared to perform edge optimization on each of the magnification unit images, and each of the magnification unit images is reduced by the preset factor to obtain an optimized unit image; Based on the overlapping areas between each pair of the optimized unit maps, each optimized unit map is processed to obtain a processed unit map, and the processed unit maps are combined into a target map.

2. The map construction optimization method as described in claim 1, characterized in that, The step of processing each optimized unit graph to obtain a processed unit graph based on the overlapping areas between each pair of optimized unit graphs includes: Select the first unit chart group from each of the aforementioned optimized unit charts; Based on the overlapping areas between each pair of optimized unit graphs in the first unit graph group, the first unit graph group is processed to obtain the first processed unit graph group. In each of the optimization unit graphs, select a second unit graph that is adjacent to the first processing unit graph group; Based on the first processing unit graph group, the second unit graph is processed to obtain a second processing unit graph, and the second processing unit graph is added to the first processing unit graph group. Return to the step of selecting a second unit graph adjacent to the first processing unit graph group in each of the optimized unit graphs, and proceed with subsequent steps until all of the optimized unit graphs have been selected.

3. The map construction optimization method as described in claim 2, characterized in that, The step of selecting the first unit chart group in each of the optimized unit charts includes: Obtain the relative position information of each optimized unit map in the global map constructed by the preset mapping algorithm; In each of the optimized unit maps, select the first unit map group whose relative position information satisfies the preset relative position conditions.

4. The map construction optimization method as described in claim 2, characterized in that, The first unit graph group includes a first unit subgraph and a second unit subgraph, and the first processing unit graph group includes a first processing unit subgraph and a second processing unit subgraph. The step of processing the first unit graph group based on the overlapping areas between each pair of optimized unit graphs in the first unit graph group to obtain the first processed unit graph group includes: The first unit subgraph is converted into first point cloud data, and the second unit subgraph is converted into second point cloud data; Obtain the third point cloud data corresponding to the first unit subgraph and the fourth point cloud data corresponding to the second unit subgraph within the overlapping area between the first unit subgraph and the second unit subgraph; Based on the first conversion relationship between the third point cloud data and the fourth point cloud data, and the second conversion relationship between the first unit sub-graph and the global map constructed by the preset mapping algorithm, a third conversion relationship between the second unit sub-graph and the global map is determined. According to the second transformation relationship, the first point cloud data is transformed into the fifth point cloud data to obtain the first processing unit subgraph, and according to the third transformation relationship, the second point cloud data is transformed into the sixth point cloud data to obtain the second processing unit subgraph.

5. The map construction optimization method as described in claim 1, characterized in that, The step of combining the processed unit maps into a target map includes: The processing unit maps are clustered and combined to obtain a cluster map; The clustered map is then converted into a two-dimensional map to obtain the target map.

6. A map building optimization device, characterized in that, The map building optimization device includes: The acquisition module is used to acquire at least two unit maps generated by a preset mapping algorithm based on laser data; The optimization module is used to perform edge optimization on each of the unit graphs to obtain an optimized unit graph; The processing module is used to process each of the optimization unit maps according to the overlapping areas between each pair of optimization unit maps to obtain a processed unit map, and to combine the processed unit maps into a target map.

7. An electronic device, characterized in that, The electronic device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the steps of the map building optimization method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program for implementing a map building optimization method, which is executed by a processor to implement the steps of the map building optimization method as described in any one of claims 1 to 5.

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