A texture map deduplication method, device and storage medium

Abstract

The application discloses a texture map deduplication method, device and storage medium. The method comprises the following steps: acquiring a plurality of groups of multi-source data collected by a map collection device during movement, each group of multi-source data comprising a texture image and laser cloud data collected at a collection time; obtaining a map frame corresponding to each collection time by using at least the texture image corresponding to each collection time; comparing laser cloud features and texture features of different map frames to find out repeated frames meeting similarity requirements from each map frame; and using each map frame except the repeated frames to form a texture map used for positioning and / or repositioning a mobile device. In this way, the application can realize texture map deduplication, thereby improving the accuracy of navigation positioning using the texture map.

Description

Technical Field

[0001] This application relates to the field of visual navigation and positioning technology, specifically to a method, device, and storage medium for deduplication of texture maps. Background Technology

[0002] Indoor navigation technologies for ground mobile robots are mainly divided into active navigation such as laser navigation and visual navigation, and passive navigation using base station networking types such as WIFI (Wireless Fidelity), UWB (Ultra Wideband), and Bluetooth.

[0003] Laser navigation technology has been developed earlier and is widely used, but single-line lasers provide limited information and are highly dependent on the environment, typically requiring the use of reflectors or reflective stickers for navigation and positioning. Multi-line lasers generate large amounts of data, necessitating the processing of massive point cloud data, placing high demands on robot platform performance, and are relatively expensive. Navigation methods such as Wi-Fi, UWB, and Bluetooth are lower in cost and more scalable, but are susceptible to obstruction by debris and signal interference, performing poorly in high-precision scenarios. Visual navigation has seen rapid development recently, with QR code navigation being a typical example, widely used in intelligent warehousing and logistics. However, QR codes are easily contaminated and damaged, and their installation affects the aesthetics of the site. Existing technologies can use ground texture information for navigation and positioning, eliminating the need for additional markers and mitigating the impact on site aesthetics to some extent.

[0004] However, in real-world applications, in large-scale texture navigation environments, route lengths can reach thousands or even tens of thousands of meters, leading to a rapid increase in the texture feature database and increasing the likelihood of similar visual texture features in the global map. At the same time, in specific scenarios (such as artificial tiles, artificial floors, etc.), ground textures are repetitive and similar due to the use of similar printing templates, which can lead to technical problems such as changes in positioning results and errors in repositioning results in severe cases. Summary of the Invention

[0005] To address the aforementioned technical problems, this application provides a texture map deduplication method, device, and storage medium, which at least solves the problem in related technologies that ground textures have a certain degree of repetition and similarity, which can lead to changes in positioning results and errors in repositioning results in severe cases.

[0006] According to an embodiment of the present invention, a texture map deduplication method is provided, comprising: Acquire several sets of multi-source data collected by the map acquisition device during its movement. Each set of multi-source data includes texture images and laser cloud data acquired at a certain acquisition time. At least the texture images corresponding to each acquisition time are used to obtain the map frames corresponding to each acquisition time; The laser cloud features and texture features of different map frames are compared to find duplicate frames that meet the similarity requirements from each map frame. The laser cloud features are determined based on laser source data at the same acquisition time corresponding to the map frame, and the texture features are determined based on texture images at the same acquisition time corresponding to the map frame. Using the map frames other than the repeating frames, a texture map is constructed for locating and / or relocating the mobile device.

[0007] To solve the above-mentioned technical problems, one technical solution adopted in this application is to provide an electronic device, including a memory and a processor, wherein the memory is used to store a computer program, and when the computer program is executed by the processor, it is used to implement the texture map deduplication method in the above-mentioned technical solution.

[0008] To solve the above-mentioned technical problems, one technical solution adopted in this application is to provide a computer-readable storage medium for storing a computer program, which, when executed by a processor, is used to implement the texture map deduplication method in the above-mentioned technical solution.

[0009] Through the above scheme, the texture map deduplication method provided in this application acquires several sets of multi-source data collected by the map acquisition device during its movement. It utilizes at least the texture images corresponding to each acquisition time to obtain map frames corresponding to each acquisition time. By comparing the laser cloud features and texture features of different map frames, duplicate frames that meet the similarity requirements are found from each map frame. The map frames other than the duplicate frames are used to form a texture map for positioning and / or repositioning of mobile devices. In this way, by combining multi-source data to perform global similarity judgment and local deduplication screening of texture map data, this application reduces the possibility of global similarity of texture features and can complete the screening of texture map data at the end of texture data acquisition, thereby improving the accuracy of navigation and positioning using texture maps. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein: Figure 1 This is a flowchart illustrating an embodiment of the texture map deduplication method provided in this application; Figure 2 This is a flowchart illustrating another embodiment of the texture map deduplication method provided in this application; Figure 3This is a schematic diagram of the structure of an embodiment of the electronic device provided in this application; Figure 4 This is a schematic diagram of an embodiment of the computer-readable storage medium provided in this application. Detailed Implementation

[0011] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be particularly noted that the following embodiments are for illustrative purposes only and do not limit the scope of the application. Similarly, the following embodiments are only some, not all, embodiments of the present application, and all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of the present application.

[0012] In this application, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0013] It should be noted that in the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such processes, methods, products, or apparatus.

[0014] Please see Figure 1 , Figure 1 This is a flowchart illustrating an embodiment of the texture map deduplication method provided in this application. It should be noted that if substantially the same result is obtained, this embodiment does not necessarily replace it. Figure 1 The illustrated process sequence is limited. For example... Figure 1 As shown, this embodiment includes: S110: Acquire several sets of multi-source data collected by the map acquisition device during its movement.

[0015] Map acquisition equipment refers to specialized equipment that integrates at least an image acquisition module, a lidar module, an odometer module, and a data processing module, used to acquire geospatial data. Multi-source data refers to a collection of geographic data from different but related sources acquired by the map acquisition equipment at the same acquisition time. Each set of multi-source data includes texture images, lidar cloud data, odometer data, etc., and each type of data is associated through a timestamp.

[0016] Among the multiple sets of multi-source data collected by the map acquisition device during its movement, each set includes texture images and laser cloud data acquired at a single acquisition moment. Texture images refer to two-dimensional image data captured by the image acquisition module of the map acquisition device, reflecting the surface texture characteristics of a geographic scene, and containing information such as the color, outline, and details of objects within the scene. Laser cloud data refers to three-dimensional point cloud data generated by the lidar module of the map acquisition device after emitting laser beams and receiving reflected signals; it can be used to construct the three-dimensional geometric structure of the geographic scene.

[0017] Each set of multi-source data may also include odometer data acquired at a single acquisition time. Odometer data refers to parameter data reflecting the movement status of the device, collected by the odometer module of the map acquisition equipment. It includes at least the device's real-time position, speed, and heading angle. It can be used to correlate multi-source data from different acquisition times, providing crucial location reference information for accurate map drawing and data fusion.

[0018] In one embodiment, the data collected by the map acquisition device during its movement is acquired from multiple sources. The data collected at different acquisition times are then time-aligned to obtain a set of multiple sources collected at the same acquisition time.

[0019] By acquiring multi-source data, the limitations of traditional single-source data in map production can be overcome. For example, texture images can supplement the visual details of the scene and avoid the problems of LiDAR cloud data being textureless and difficult to identify; LiDAR cloud data can provide three-dimensional geometric structures to make up for the defects of texture images being shallow and difficult to model; odometry data enables the spatial stitching of multiple sets of multi-source data to ensure the continuity of the map.

[0020] S120: At least using the texture images corresponding to each acquisition time, obtain the map frames corresponding to each acquisition time.

[0021] A map frame refers to a two-dimensional map unit with geographic coordinate attributes generated by combining a texture image at a single acquisition time with corresponding odometer data for spatial positioning and geometric correction. Each map frame contains texture information and precise spatial location, and can serve as a basic building block for high-precision maps. Adjacent map frames are stitched together using spatial coordinates to form a complete map.

[0022] In one embodiment, for each acquisition time, the texture image at that acquisition time and at least one other source data at that acquisition time are fused to obtain a map frame for that acquisition time. In another embodiment, preprocessed multi-source data at the same acquisition time are first extracted, such as distortion-corrected environmental texture images, denoised laser cloud data, odometer data containing position and orientation, and landmark identification data. Using the odometer coordinate system as a reference, the laser cloud data and landmark coordinates are converted into geographic coordinates using a specified formula to achieve unification. Then, the texture image and landmark feature points are detected and extracted to generate descriptors. The edge feature points of the laser cloud data are extracted and projected to extract descriptors, establishing a coordinate mapping relationship between the three types of data. Subsequently, based on the mapping, texture color and landmark navigation attributes are assigned to the three-dimensional points of the laser cloud to generate a three-dimensional point cloud with navigation attributes. The geographic boundaries are determined by combining the odometer and landmark data, and the fused metadata is supplemented to obtain a fused map frame, thus completing the acquisition of the map frame at the acquisition time.

[0023] S130: Compare the laser cloud features and texture features of different map frames to find duplicate frames that meet the similarity requirements from each map frame.

[0024] The laser cloud features of a map frame are determined based on laser source data acquired at the same acquisition time as the map frame, while the texture features are determined based on texture images acquired at the same acquisition time as the map frame. By comparing the laser cloud features and texture features of different map frames, duplicate frames that meet the similarity requirements are identified from each map frame.

[0025] In one embodiment, each map frame is taken as the current frame. From the remaining map frames other than the current frame, several frames to be processed that are located within a preset distance range of the current frame are searched. The laser cloud features and texture features of the current frame and the frames to be processed are compared to obtain the feature similarity between the current frame and the frames to be processed. The frames to be processed and the current frame with feature similarity greater than the similarity threshold are determined as duplicate frames that meet the similarity requirements.

[0026] In one example, a spatial partitioning tree (KD-Tree) structure is used to perform a nearest neighbor search within a preset distance range on the current frame to obtain several frames to be processed. The spatial partitioning tree structure is constructed based on the odometry data corresponding to each map frame. For the several frames to be processed obtained from the nearest neighbor search of the current frame, the laser cloud feature matching degree between the laser cloud features of the current frame and the laser cloud features of the frames to be processed is obtained in sequence, as is the texture feature matching degree between the texture features of the current frame and the texture features of the frames to be processed. The laser cloud feature matching degree and the texture feature matching degree are then weighted and fused to obtain the feature similarity between the current frame and the frames to be processed.

[0027] If the feature similarity between the current frame and the frame to be processed is greater than the similarity threshold, then the frame to be processed is considered similar to the current frame, and both frames can be marked as duplicate frames and will not be used in subsequent relocation, thus achieving the effect of deduplication of location data. If the feature similarity between the current frame and the frame to be processed is not greater than the similarity threshold, then the frame to be processed is considered dissimilar to the current frame, and the feature similarity calculation between the next frame to be processed and the current frame is performed sequentially.

[0028] In response to the existence of a frame to be processed with a feature similarity greater than the similarity threshold, the feature similarity calculation is no longer performed on the remaining frames to be processed in the current frame. Instead, a new current frame is selected from the remaining map frames that were not previously used as the current frame, and the process of searching for several frames to be processed within the preset distance range of the current frame from the remaining map frames other than the current frame and the subsequent steps are re-executed.

[0029] If there is no frame with a feature similarity greater than the similarity threshold among the several frames to be processed corresponding to the current frame, then continue to sequentially search for several frames to be processed within the preset distance range of the current frame from the remaining map frames other than the current frame and their subsequent steps, until all map frames have been traversed, so as to obtain all the duplicate frames in the map frames acquired and fused by the current map acquisition device during the movement.

[0030] In one implementation, for map frames already marked as duplicate frames, a duplicate frame queue can be constructed. When performing nearest neighbor searches based on the current frame to obtain several frames to be processed, the duplicate frames can be removed from this queue to improve computational efficiency. In other implementations, for pairwise map frames whose feature similarity has already been calculated, the calculation results can be retained to avoid redundant calculations.

[0031] S140: Using map frames other than repeating frames, a texture map is constructed for locating and / or relocating the mobile device.

[0032] As the mobile device moves within the target environment, it continuously acquires image frames of the surrounding environment. These image frames contain texture details of the environment, such as the texture of the ground. Within the acquired image sequence, there may be some repetitive or highly similar image frames, which can affect the mobile device's localization and / or relocalization, and also increase the burden of data processing.

[0033] Therefore, the texture map deduplication method provided in this embodiment can identify and mark these duplicate frames, retain map frames with unique texture information, that is, use each map frame other than the duplicate frames to form a texture map for positioning and / or repositioning of mobile devices.

[0034] In one embodiment, when a mobile device needs to determine its current location, it can capture the current image frame, extract its texture features, and match them with features in the texture map. By finding the map frame or feature point that is most similar to the current image frame, the location of the mobile device in the texture map can be determined.

[0035] In one embodiment, during the operation of a mobile device, various factors such as sensor errors and environmental changes may lead to the accumulation of positioning errors. Relocation refers to the process by which a mobile device, when it detects a large positioning error or loses its location, re-matches itself with a texture map to restore accurate location information. For example, when a mobile device enters a new area or after operating for a period of time, it can redetermine its location by searching for features in the texture map.

[0036] Please see Figure 2 , Figure 2 This is a flowchart illustrating another embodiment of the texture map deduplication method provided in this application. It should be noted that if substantially the same result is obtained, this embodiment does not necessarily replace it with a different method. Figure 2 The illustrated process sequence is limited. For example... Figure 2 As shown, this embodiment includes: S201: Collect texture data of the target environment.

[0037] Texture data of the target environment is collected using map acquisition equipment. In one embodiment, guide lines containing first landmarks (used to indicate the direction of travel, such as L-shaped turns, T-shaped U-turns / termination identifiers) and second landmarks (used to trigger visual navigation template acquisition, such as cross-shaped straight-ahead, L-shaped turn identifiers) are laid along the robot's task route. Images containing each landmark are first acquired, preprocessed, and then input into a neural network-multilayer perceptron (ANN_MLP) to train an identifier recognition model. When the robot moves, it acquires images of its movement using a camera. The trained model identifies identifiers in the images. If the first landmark is identified, the direction of travel is determined and the image is stored. If the second landmark is identified, the direction is maintained and the movement continues, and the image is stored. Feature points are extracted from the stored images, and after matching, images containing the same landmarks are stitched together. Using the landmark coordinates as the center, the images are cropped and stitched according to the length and width of a single frame to obtain a visual navigation image template. The template's feature point set and descriptors are detected, stored using a Keyframe structure, and a mapping relationship with the landmark numbers is established. After serialized binary encoding and compression, a robot image template library is formed, completing the texture data acquisition.

[0038] S202: Associate laser point cloud data and odometer data.

[0039] After acquiring texture data, laser point cloud data, and odometer data collected by the map acquisition device during its movement, the three types of data are correlated, and the texture data, laser point cloud data, and odometer data are timestamped together.

[0040] S203: Construct fused data and obtain the queue of map data to be processed.

[0041] The texture, laser, and odometry data associated with each moment are bound together as the same frame of data, thus constructing fused data and pushing it into the queue of map data to be processed, thereby obtaining the queue of map data to be processed for the target environment.

[0042] S204: Construct a spatial partitioning tree structure.

[0043] After the multi-source data acquisition and data fusion are completed, a spatial partition tree structure (KD-Tree) is constructed for all the acquired fused data based on the odometry values, so as to find the nearest neighbor of each map data to be processed when performing texture map deduplication.

[0044] S205: Iterate through the map data to be processed in sequence.

[0045] For each piece of map data to be processed in the queue, iterate through it sequentially and perform the following steps S206-S210 for each frame of map data to be processed.

[0046] S206: Filter the data to be analyzed based on the location prediction range threshold.

[0047] Determine a localization prediction range threshold r For each frame of map data to be processed Radius r Nearest neighbor search, assuming a radius of [missing information] in the vicinity of the current frame. r All data to be analyzed were found within the specified range. ,Right now and The current map data to be processed is obtained by satisfying the constraints of the following formula (1). The corresponding data to be analyzed .

[0048] (1) in, odom This is the odometer data for the corresponding frame. r Based on the allowable deviation range for positioning and repositioning, preferably, a radius is specified. r The value is 2 meters.

[0049] S207: Calculate the feature similarity of the data to be analyzed.

[0050] For each piece of data to be analyzed Calculate the data to be analyzed separately. With the current map data to be processed Feature similarity score between The specific calculation method is shown in formula (2) below, where, (Range 0~1) represents the data to be analyzed. With the current map data to be processed The laser matching similarity score, The weighting parameter for this score (range 0~1). (Range 0~1) represents the data to be analyzed. With the current map data to be processed Texture feature matching similarity score, The weighting parameter for this score.

[0051] (2) In one embodiment, similarity judgment is achieved using visual texture as the core. The environment image is adaptively split using a quadtree algorithm, dividing it into image patches of different scales to balance local details and global structure. Then, the FAST algorithm is used to detect feature points in each image patch, and the rBRIEF algorithm is used to binary encode the feature point pairs, generating a compact texture feature representation. Finally, the similarity is measured by calculating the Hamming distance between the binary feature codes of the current environment image and pre-stored templates in the texture information database (a smaller Hamming distance indicates higher similarity). Simultaneously, the consistency of the distance between the detected feature points in the current frame and the previous frame is combined to further filter matching point pairs, improving the accuracy of the judgment.

[0052] In one embodiment, the laser matching similarity is determined using ICP (Iterative Nearest Point) registration. For example, the laser point cloud to be matched is first divided into a source point cloud (currently acquired data) and a target point cloud (reference data). Using structures such as KD-Tree, the closest Euclidean distance point in the target point cloud is found for each point in the source point cloud, forming preliminary corresponding point pairs. Then, erroneous point pairs are eliminated using criteria such as distance thresholds and the angle between normal vectors, retaining valid point pairs with matching geometric features. Subsequently, based on the valid point pairs, the optimal rigid body transformation (rotation matrix and translation vector) is calculated using methods such as Singular Value Decomposition (SVD) to minimize the distance error between point pairs. The above steps of "finding corresponding points - eliminating errors - calculating transformations" are repeated until the error change is less than the threshold or the maximum number of iterations is reached. Finally, the laser feature similarity is measured by the converged error value; the smaller the error, the higher the similarity.

[0053] It should be noted that the calculation methods for laser matching similarity scores and texture feature similarity scores are not limited to those described in the above embodiments, and are not limited here.

[0054] S208: Determine whether the feature similarity is greater than the similarity threshold.

[0055] Define a similarity threshold ScoreThreshold If the feature similarity score of the data to be analyzed Then the data to be analyzed is considered to be With the current map data to be processed If the conditions are similar, proceed to step S209; otherwise, proceed to step S210.

[0056] S209: Perform deduplication operation.

[0057] For data to be analyzed where the feature similarity is greater than the similarity threshold The current map data to be processed With the data to be analyzed All frames are marked as duplicates so that they will not be used in subsequent relocation, thus achieving the effect of deduplication of the texture map. At the same time, the operation on the current data queue to be analyzed is terminated, and the process proceeds to step S210.

[0058] S210: Determine whether all pending map data has been processed.

[0059] If there are still unprocessed map data in the queue of pending map data, return to step S205 to proceed with the next frame of pending map data. The processing.

[0060] If no unchecked map data is found in the queue of map data to be processed, it is determined that all map data to be processed has been traversed, and the texture map deduplication step ends. The texture map is then constructed using all map frames except for duplicate frames for positioning and / or repositioning of the mobile device.

[0061] Please see Figure 3 , Figure 3 This is a schematic diagram of an embodiment of the electronic device provided in this application. The electronic device 60 includes a memory 61 and a processor 62 that are interconnected. The memory 61 is used to store a computer program. When the computer program is executed by the processor 62, it is used to implement the texture map deduplication method in the above embodiment.

[0062] The methods described in the above embodiments can exist in the form of a computer program; therefore, this application proposes a computer-readable storage medium. Please refer to [link / reference needed]. Figure 4 , Figure 4 This is a schematic diagram of an embodiment of a computer-readable storage medium provided in this application. The computer-readable storage medium 80 is used to store a computer program 81, which can be executed to implement the texture map deduplication method in the above embodiment.

[0063] The computer-readable storage medium 80 can be any medium capable of storing program code, such as a server, USB flash drive, external hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0064] The above description is merely an embodiment of this application and does 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 protection scope of this application.

Claims

1. A method for deduplicating texture maps, characterized in that, The method includes: Acquire several sets of multi-source data collected by the map acquisition device during its movement. Each set of multi-source data includes texture images and laser cloud data acquired at a certain acquisition time. At least the texture images corresponding to each acquisition time are used to obtain the map frames corresponding to each acquisition time; The laser cloud features and texture features of different map frames are compared to find duplicate frames that meet the similarity requirements from each map frame. The laser cloud features are determined based on laser source data at the same acquisition time corresponding to the map frame, and the texture features are determined based on texture images at the same acquisition time corresponding to the map frame. Using the map frames other than the repeated frames, a texture map is constructed for locating and / or relocating the mobile device. The comparison of laser cloud features and texture features of different map frames to find duplicate frames that meet the similarity requirements from each map frame includes: Each of the aforementioned map frames is taken as the current frame; From the map frames other than the current frame, search for several frames to be processed that are located within a preset distance range of the current frame; The laser cloud features and texture features of the current frame and the frame to be processed are compared to obtain the feature similarity between the current frame and the frame to be processed; The frames to be processed and the current frame whose feature similarity is greater than the similarity threshold are identified as duplicate frames that meet the similarity requirements. The step of searching for a number of frames to be processed from the remaining map frames other than the current frame, which are located within a preset distance range of the current frame, includes: Using a spatial partition tree structure, a nearest neighbor search within a preset distance range is performed on the current frame to obtain the plurality of frames to be processed, wherein the spatial partition tree structure is constructed based on the odometry data corresponding to each map frame.

2. The method according to claim 1, characterized in that, The step of comparing the laser cloud features and texture features of the current frame and the frame to be processed to obtain the feature similarity between the current frame and the frame to be processed includes: The laser cloud feature matching degree between the laser cloud feature of the current frame and the laser cloud feature of the frame to be processed is obtained, and the texture feature matching degree between the texture feature of the current frame and the texture feature of the frame to be processed is obtained. The laser cloud feature matching degree and the texture feature matching degree are weighted and fused to obtain the feature similarity between the current frame and the frame to be processed.

3. The method according to claim 1, characterized in that, After determining the frames to be processed and the current frame whose feature similarity is greater than the similarity threshold as duplicate frames that meet the similarity requirement, the method further includes: In response to the existence of a frame to be processed with a feature similarity greater than a similarity threshold, the feature similarity calculation is no longer performed on the remaining frames to be processed of the current frame. Instead, a new current frame is selected from the remaining map frames that were never used as the current frame. The steps of searching for several frames to be processed within a preset distance range of the current frame from the remaining map frames other than the current frame and subsequent steps are then re-executed. And / or, after searching for a number of frames to be processed within a preset distance range of the current frame from the remaining map frames other than the current frame, and before comparing the laser cloud features and texture features of the current frame and the frames to be processed to obtain the feature similarity between the current frame and the frames to be processed, the method further includes: From the plurality of frames to be processed, remove the frames that have been identified as duplicate frames.

4. The method according to claim 1, characterized in that, Each set of multi-source data includes texture images, laser cloud data, and odometry data acquired at a single acquisition time. And / or, the acquisition of map data acquisition devices during movement includes several sets of multi-source data, including: Acquire data from various sources collected at different collection times; Time alignment is performed on the source data collected at different acquisition times to obtain a set of multi-source data collected at the same acquisition time.

5. The method according to claim 1, characterized in that, The step of obtaining map frames corresponding to each acquisition time by utilizing at least the texture images corresponding to each acquisition time includes: For each acquisition time, the texture image of that acquisition time and at least one other source data of that acquisition time are fused to obtain the map frame of that acquisition time.

6. The method according to claim 1, characterized in that, Before constructing a texture map for locating and / or relocating a mobile device using the map frames other than the repeating frames, the method further includes: Construct a texture map using each of the aforementioned map frames; The step of constructing a texture map for locating and / or relocating a mobile device using all the map frames other than the repeating frames includes: A marker is added to the repeating frames in the texture map, wherein the map frames to which the marker is added are not used for positioning and / or repositioning of the mobile device.

7. An electronic device, characterized in that, The electronic device includes a processor and a memory, the processor being coupled to the memory, the processor being configured to perform one or more steps of the texture map deduplication method according to any one of claims 1 to 6 based on instructions stored in the memory.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that is executed by a processor to implement the steps of the texture map deduplication method as described in any one of claims 1 to 6.

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