Robot positioning method and system based on simple retro-reflective column map

Abstract

The application provides a robot positioning method and system based on a simple retroreflective column map, and belongs to the technical field of robot navigation positioning. The positioning method comprises the following steps: obtaining a plurality of sub-maps obtained through repeated static mapping, and there are at least three retroreflective columns that are the same among the sub-maps; fusing all the sub-maps to obtain a total map containing information of all the sub-maps; extracting retroreflective column information from obtained radar information; matching the retroreflective column information according to the total map to obtain a positioning result; indexing to a specific sub-map according to the matching result of the retroreflective column in the total map; and performing multilateration in the indexed sub-map to obtain a robot positioning result. In the positioning method, data in the sub-maps is used, so that the problem of abnormal retroreflective column construction in the total map optimization process is reduced, and the stability of the positioning is ensured.

Description

Technical Field

[0001] This invention relates to the field of robot navigation and positioning technology, and in particular to a robot positioning method and system based on a simple reflective bob map. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] With the advancement of industrial automation and intelligence, Automated Guided Vehicles (AGVs) are gradually becoming important supporting equipment for the "material flow" in the logistics industry, smart factories, and flexible manufacturing workshops. They can replace manual labor in handling various types of goods, saving labor costs and improving work efficiency. Path tracking is a key technology for AGV motion control, and its accuracy plays a crucial role in expanding the working range and improving efficiency of AGVs. High-precision path tracking requires high accuracy in AGV positioning.

[0004] Compared to visual navigation, laser navigation is more expensive, but it also has higher positioning accuracy, allows for flexible changes in the driving path, and can operate in the dark, thus attracting widespread attention. The laser radar positioning method based on reflective pillars is suitable for the positioning of AGVs or robots in known environments. It uses laser radar to emit laser beams to scan the surrounding environment and collects the laser beam information reflected by reflective pillars. The position of the AGV or robot is further solved based on the matching algorithm.

[0005] The inventors discovered that when positioning AGVs or robots, the overall diagram is often used directly. However, during the optimization of the overall diagram, there are often problems with the abnormal construction of reflective pillars, which makes it impossible to achieve accurate positioning of AGVs or robots. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a robot localization method and system based on a simplified reflective pillar map. By using data from the sub-map during localization, the problem of abnormal reflective pillar construction during the overall map optimization process is reduced, thus ensuring stable localization.

[0007] To achieve the above objectives, the present invention adopts the following technical solution:

[0008] In a first aspect, the present invention provides a robot localization method based on a simplified reflective pillar map.

[0009] A robot localization method based on a simplified reflective pillar map includes the following steps:

[0010] Multiple sub-maps obtained through repeated static mapping are acquired. Each sub-map has at least three identical reflective pillars. All sub-maps are then merged to obtain a master map containing information from all sub-maps.

[0011] The reflective column information is extracted from the acquired radar information, and the positioning result is obtained by matching it with the overall map. Based on the matching result of the reflective column in the overall map, the robot is indexed to a specific sub-map and then subjected to polygonal positioning in the indexed sub-map to obtain the robot positioning result.

[0012] As a further limitation of the first aspect of the invention, all subgraphs are merged to obtain a general graph containing information from all subgraphs, including:

[0013] Based on data from different robot positions, multiple sub-graphs are constructed under static conditions. Constraint relationships are established between the sub-graphs using a polygonal localization method. All sub-graphs are then transferred to the global coordinate system.

[0014] The reflection pillars of each subgraph are clustered to form the overall graph. The subgraphs participating in the construction of the overall graph are related to the overall graph in order. Figure 1 Save it.

[0015] As a further limitation of the first aspect of the invention, the construction of the general layout includes:

[0016] Step 1: For any subgraph, determine if it is the first subgraph. If so, save it to the overall graph list and set the global pose flag of the subgraph to true, then proceed to Step 4; otherwise, proceed to Step 2.

[0017] Step 2: Determine whether there are constraints on the subgraph in the overall diagram list and whether global coordinates can be obtained based on polygonal positioning. If not, save this subgraph to the overall diagram list and set the global pose flag of the subgraph to false. If yes, proceed to Step 3.

[0018] Step 3: Save the constraint relationship between this sub-graph and the sub-graphs in the overall drawing list, determine the identification code of the reflector, save this sub-graph to the overall drawing list, and set the global pose flag of this sub-graph to true, then proceed to step 4;

[0019] Step 4: Construct least squares relationships based on constraints, solve for the reflector pillars and sub-map world coordinates that minimize error, save the overall map, assign an identification code to each reflector pillar, and proceed to Step 5:

[0020] Step 5: Determine if the global pose flag of the sub-image is true. If it is, discard the sub-image. If not, record the identification code of the sub-image, perform polygonal positioning, and mark the global coordinates of the reflector in the sub-image and the corresponding identification code in the global coordinate map.

[0021] As a further limitation of the first aspect of the invention, the plurality of sub-graphs obtained by repeated static mapping include:

[0022] Acquire stationary laser data at different positions of the robot, filter the stationary laser data, identify reflective pillars in the filtered stationary laser data, delete mislabeled reflective pillars, and obtain sub-images corresponding to different positions of the robot.

[0023] As a further limitation of the first aspect of the invention, the process of recognizing reflective columns includes:

[0024] The laser points that hit the reflective column are selected based on the light intensity.

[0025] If the number of laser points clustered exceeds the set threshold, discard the cluster; otherwise, determine it as a group of reflective pillars and select the point with the highest light intensity to extend the radius of the reflective pillar to obtain the center point of the reflective pillar.

[0026] As a further limitation of the first aspect of the present invention, reflective column information is extracted from the acquired radar information, and a positioning result is obtained by matching it according to the overall map. Based on the matching result of the reflective columns in the overall map, a specific sub-map is indexed, and polygonal positioning is performed in the indexed sub-map to obtain the robot positioning result, including:

[0027] Perform reflective column identification and determine if the number of reflective columns is less than 3. If so, do not perform polygon positioning; otherwise, proceed to the next step.

[0028] Determine whether the location information is obtained in the overall map. If not, discard the identified reflective column data. If so, obtain the identification code of the reflective column involved in the location in the overall map.

[0029] Based on the reflective column identification codes in the main map that exist in the sub-map, find the sub-map with the most matches, perform polygonal localization in the sub-map, and obtain the robot localization result.

[0030] Secondly, the present invention provides a robot positioning system based on a simplified reflective pillar map.

[0031] A robot localization system based on a simple reflective pillar map includes:

[0032] The overall map construction module is configured to: acquire multiple sub-maps obtained through repeated static mapping, with at least three identical reflective pillars between each sub-map, merge all sub-maps to obtain an overall map containing information from all sub-maps;

[0033] The robot localization module is configured to: extract reflective column information from the acquired radar information, match it with the overall map to obtain the localization result, index the specific sub-map based on the matching result of the reflective columns in the overall map, and perform polygon localization in the indexed sub-map to obtain the robot localization result.

[0034] As a further limitation of the second aspect of the present invention, in the general layout construction module, the construction of the general layout includes:

[0035] Step 1: For any subgraph, determine if it is the first subgraph. If so, save it to the overall graph list and set the global pose flag of the subgraph to true, then proceed to Step 4; otherwise, proceed to Step 2.

[0036] Step 2: Determine whether there are constraints on the subgraph in the overall diagram list and whether global coordinates can be obtained based on polygonal positioning. If not, save this subgraph to the overall diagram list and set the global pose flag of the subgraph to false. If yes, proceed to Step 3.

[0037] Step 3: Save the constraint relationship between this sub-graph and the sub-graphs in the overall drawing list, determine the identification code of the reflector, save this sub-graph to the overall drawing list, and set the global pose flag of this sub-graph to true, then proceed to step 4;

[0038] Step 4: Construct least squares relationships based on constraints, solve for the reflector pillars and sub-map world coordinates that minimize error, save the overall map, assign an identification code to each reflector pillar, and proceed to Step 5:

[0039] Step 5: Determine if the global pose flag of the sub-image is true. If it is, discard the sub-image. If not, record the identification code of the sub-image, perform polygonal positioning, and mark the global coordinates of the reflector in the sub-image and the corresponding identification code in the global coordinate map.

[0040] Thirdly, the present invention provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps of the robot localization method based on a simplified reflective pillar map as described in the first aspect of the present invention.

[0041] Fourthly, the present invention provides an electronic device, including a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps in the robot localization method based on a simplified reflective pillar map as described in the first aspect of the present invention.

[0042] Compared with the prior art, the beneficial effects of the present invention are:

[0043] 1. This invention innovatively proposes a robot localization method based on a simplified reflective pillar map. During the construction of the sub-map, misidentified reflective pillars are removed, which can effectively reduce the risk of misidentification and provide more accurate data for subsequent loop closure detection.

[0044] 2. This invention innovatively proposes a robot localization method based on a simplified reflective pillar map. By using data from the sub-map during localization, the problem of abnormal reflective pillar construction during the overall map optimization process is reduced, thus ensuring stable localization. Attached Figure Description

[0045] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0046] Figure 1 This is the logic diagram for constructing reflective columns provided in Embodiment 1 of the present invention;

[0047] Figure 2 This is a schematic diagram of the polygonal positioning principle provided in Embodiment 1 of the present invention;

[0048] Figure 3 This is a schematic diagram of the polygonal localization algorithm provided in Embodiment 1 of the present invention;

[0049] Figure 4 This is a schematic diagram of the construction of the reflective column diagram provided in Embodiment 1 of the present invention;

[0050] Figure 5 This is a schematic diagram of the reflective column recognition process provided in Embodiment 1 of the present invention;

[0051] Figure 6 This is a schematic diagram of clustering threshold calculation provided in Embodiment 1 of the present invention;

[0052] Figure 7 This is a schematic diagram of the overall map construction and map information storage of reflective pillars provided in Embodiment 1 of the present invention;

[0053] Figure 8 This is a schematic diagram of the constraint relationship construction provided in Embodiment 1 of the present invention; Detailed Implementation

[0054] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0055] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0056] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0057] Example 1:

[0058] Embodiment 1 of the present invention provides a robot localization method based on a simplified reflective bar map, comprising the following steps:

[0059] S1: When the machine is stationary, record multiple frames of still data, fit the data of the multiple frames of still data to construct sub-maps, and remove the incorrectly identified reflective pillars through the host computer to construct accurate sub-map information. By repeating the static mapping process, multiple sub-maps are constructed. There are identical reflective pillars (more than 3 pillars) between the sub-maps. All sub-maps are merged to finally obtain the overall map information. The sub-map information is also saved into the map information.

[0060] S2: The robot or AGV extracts the reflective column information from the acquired radar information. The reflective column information is matched with the overall map to obtain the positioning result. Based on the matching result of the reflective columns in the overall map, it is indexed to the specific sub-map and the positioning information is output in the sub-map.

[0061] Specifically, in S1, the logic diagram for constructing reflector columns is as follows: Figure 1 As shown, all subgraphs are merged to obtain a master graph containing information from all subgraphs, including:

[0062] Based on data from different robot positions, multiple sub-graphs are constructed under static conditions. Constraint relationships are established between the sub-graphs using a polygonal localization method. All sub-graphs are then transferred to the global coordinate system.

[0063] The reflection pillars of each subgraph are clustered to form the overall graph. The subgraphs participating in the construction of the overall graph are related to the overall graph in order. Figure 1 Save it.

[0064] More specifically, the principle of multilateral positioning, such as Figure 2 As shown, the total coordinates in the global coordinate system Figure 1 subgraph coordinate system Figure 1 Correspondingly, the total coordinates in the global coordinate system Figure 4 subgraph coordinate system Figure 2 Correspondingly, the total coordinates in the global coordinate system Figure 5 subgraph coordinate system Figure 3 correspond;

[0065] Because the global coordinates (X) of all effective reflectors i Y i Given the distance, subtracting any two equations from the above equations yields a linear equation about (X0, Y0). Subtracting all equations pairwise yields the following equation: Solving the linear equations using the least squares method yields the global coordinates (X0, Y0) of the laser.

[0066] The angle is determined by combining the azimuth angle of the effective reflector relative to the laser navigation device and the global coordinates, which gives the radar's orientation α0 relative to the global coordinates.

[0067] The azimuth angle α1 obtained from the first effective reflector:

[0068]

[0069] The azimuth angle α obtained from the i-th effective reflector i :

[0070]

[0071] Where, α1, ..., α i Let θ1, ..., θ2 be the radar orientation obtained from the i-th effective reflector. i , respectively, represent the orientation of the i-th effective reflector relative to the radar.

[0072] In this embodiment, multiple sub-graphs obtained through repeated static mapping are as follows: Figure 3 As shown, the construction of the reflective bar chart includes:

[0073] Acquire stationary laser data at different positions of the robot, perform KF filtering on the stationary laser data, identify reflective pillars on the filtered stationary laser data, and delete mislabeled reflective pillars (this can be done manually or by using a trained mislabeling recognition algorithm) to obtain sub-images corresponding to different positions of the robot.

[0074] In this embodiment, as Figure 4 As shown, reflective column identification includes:

[0075] The laser points that hit the reflective column are selected based on the light intensity.

[0076] If the number of laser points clustered exceeds the set threshold, discard the cluster; otherwise, determine it as a group of reflective pillars and select the point with the highest light intensity to extend the radius of the reflective pillar to obtain the center point of the reflective pillar.

[0077] like Figure 5 As shown, the method for obtaining the set threshold is given, including the following process:

[0078] Assuming the radius of the reflector is r, the laser distance is l, and the angle (radar resolution) between the two laser beams is resolution, then the threshold is set as follows:

[0079]

[0080] like Figure 6 As shown, the construction and saving of the site plan includes:

[0081] Step 1: For any subgraph, determine if it is the first subgraph. If so, save it to the overall graph list and set the global pose flag of the subgraph to true, then proceed to Step 4; otherwise, proceed to Step 2.

[0082] Step 2: Determine whether there are constraints on the subgraph in the overall diagram list and whether the global coordinates can be obtained based on polygonal positioning. If not, save the subgraph to the overall diagram list and set the global pose flag of the subgraph to false. If yes, proceed to Step 3.

[0083] Step 3: Save the constraint relationship between this subgraph i and subgraph j in the overall drawing list, determine the identification code (ID) of the reflector, save this subgraph to the overall drawing list, and set the global pose flag of this subgraph to true. Then proceed to step 4.

[0084] Step 4: Construct least squares relationships based on constraints, solve for the reflector pillars and subgraph world coordinates that minimize error, save the overall map, and assign an identification code (ID) to each reflector pillar. Then proceed to Step 5:

[0085] Step 5: Determine if the global pose flag of the sub-image is true. If it is, discard the sub-image. If not, record the identification code (ID) of the sub-image, perform polygonal localization, and mark the global coordinates of the reflector in the sub-image and the corresponding identification code (ID) in the global coordinate map.

[0086] In this embodiment, the constraint relationship is constructed as follows: Figure 7 As shown, there are similar triangles between subgraphs i and j. The resulting constraint relationships are: 3 in subgraph i and 5 in subgraph j, 2 in subgraph i and 4 in subgraph j, and 4 in subgraph i and 6 in subgraph j.

[0087] In this embodiment, the specific positioning method is as follows: Figure 8 As shown, it includes:

[0088] Perform reflective column identification and determine if the number of reflective columns is less than 3. If so, do not perform polygon positioning; otherwise, proceed to the next step.

[0089] Determine whether the location information is obtained in the overall map. If not, discard the identified reflective column data. If so, obtain the identification code of the reflective column involved in the location in the overall map.

[0090] Based on the reflective column identification codes in the main map that exist in the sub-map, find the sub-map with the most matches, perform polygonal localization in the sub-map, and obtain the robot localization result.

[0091] Example 2:

[0092] Embodiment 2 of the present invention provides a robot localization system based on a simplified reflective pillar map, comprising:

[0093] The overall map construction module is configured to: acquire multiple sub-maps obtained through repeated static mapping, with at least three identical reflective pillars between each sub-map, merge all sub-maps to obtain an overall map containing information from all sub-maps;

[0094] The robot localization module is configured to: extract reflective column information from the acquired radar information, match it with the overall map to obtain the localization result, index the specific sub-map based on the matching result of the reflective columns in the overall map, and perform polygon localization in the indexed sub-map to obtain the robot localization result.

[0095] The specific working methods of each module of the system are the same as the robot localization method based on a simplified reflective column map provided in Example 1, and will not be repeated here.

[0096] Example 3:

[0097] Embodiment 3 of the present invention provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps in the robot localization method based on a simplified reflective pillar map as described in Embodiment 1 of the present invention.

[0098] Example 4:

[0099] Embodiment 4 of the present invention provides an electronic device, including a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps in the robot localization method based on a simplified reflective column map as described in Embodiment 1 of the present invention.

[0100] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0101] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1A device that provides the functions specified in one or more boxes.

[0102] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0103] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0104] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0105] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A robot localization method based on a simplified reflective pillar map, characterized in that, The process includes the following: Obtain multiple sub-maps obtained through repeated static mapping, where each sub-map contains at least three identical reflective pillars; Multiple sub-maps obtained through repeated static mapping include: acquiring static laser data at different positions of the robot, filtering the static laser data, identifying reflective pillars in the filtered static laser data, deleting mislabeled reflective pillars, and obtaining sub-maps corresponding to different positions of the robot. By merging all subgraphs, a total graph containing information from all subgraphs is obtained; The robot extracts reflective column information from the acquired radar information, matches it against the overall map to obtain the positioning result, and indexes the specific sub-map based on the matching result of the reflective columns in the overall map. Polygonal localization is then performed in the indexed sub-map to obtain the robot's positioning result. This includes: identifying reflective columns and determining if the number of reflective columns is less than 3; if so, polygonal localization is not performed; otherwise, proceed to the next step; determining if positioning information is obtained in the overall map; if not, discarding the identified reflective column data; if so, obtaining the identification code of the reflective columns participating in positioning in the overall map; and finding the sub-map with the most matching reflective column identification codes in the sub-map, performing polygonal localization in that sub-map to obtain the robot's positioning result.

2. The robot localization method based on a simplified reflective pillar map as described in claim 1, characterized in that, By merging all subgraphs, a master graph containing information from all subgraphs is obtained, including: Based on data from different robot positions, multiple sub-graphs are constructed under static conditions. Constraint relationships are established between the sub-graphs using a polygonal localization method. All sub-graphs are then transferred to the global coordinate system. The reflective pillars of each subgraph are clustered to form the overall graph, and the subgraphs that participate in the construction of the overall graph are saved together with the overall graph in order.

3. The robot localization method based on a simplified reflective pillar map as described in claim 2, characterized in that, The construction of the site plan includes: Step 1: For any subgraph, determine if it is the first subgraph. If so, save it to the overall graph list and set the global pose flag of the subgraph to true, then proceed to Step 4; otherwise, proceed to Step 2. Step 2: Determine whether there are constraints on the subgraph in the overall diagram list and whether global coordinates can be obtained based on polygonal positioning. If not, save this subgraph to the overall diagram list and set the global pose flag of the subgraph to false. If yes, proceed to Step 3. Step 3: Save the constraint relationship between this sub-graph and the sub-graphs in the overall drawing list, determine the identification code of the reflector, save this sub-graph to the overall drawing list, and set the global pose flag of this sub-graph to true, then proceed to step 4; Step 4: Construct least squares relationships based on constraints, solve for the reflector pillars and sub-map world coordinates that minimize error, save the overall map, assign an identification code to each reflector pillar, and proceed to Step 5: Step 5: Determine if the global pose flag of the sub-image is true. If it is, discard the sub-image. If not, record the identification code of the sub-image, perform polygonal positioning, and mark the global coordinates of the reflector in the sub-image and the corresponding identification code in the global coordinate map.

4. The robot localization method based on a simplified reflective pillar map as described in claim 1, characterized in that, Reflective column identification includes: The laser points that hit the reflective column are selected based on the light intensity. If the number of laser points clustered exceeds the set threshold, discard the cluster; otherwise, determine it as a group of reflective pillars and select the point with the highest light intensity to extend the radius of the reflective pillar to obtain the center point of the reflective pillar.

5. A robot positioning system based on a simplified reflective pillar map, characterized in that, include: The overall map construction module is configured to: acquire multiple sub-maps obtained through repeated static mapping, with at least three identical reflective pillars existing between each sub-map; Multiple sub-maps obtained through repeated static mapping include: acquiring static laser data at different positions of the robot, filtering the static laser data, identifying reflective pillars in the filtered static laser data, deleting mislabeled reflective pillars, and obtaining sub-maps corresponding to different positions of the robot. By merging all subgraphs, a total graph containing information from all subgraphs is obtained; The robot localization module is configured to: extract reflective column information from the acquired radar information, match it with the overall map to obtain the localization result, index the specific sub-map based on the matching result of the reflective columns in the overall map, and perform multilateral localization in the indexed sub-map to obtain the robot localization result; including: performing reflective column identification, determining whether the number of reflective columns is less than 3, if so, not performing multilateral localization, otherwise proceeding to the next step; determining whether localization information is obtained in the overall map, if not, discarding the identified reflective column data, if so, obtaining the identification code of the reflective columns participating in localization in the overall map; and finding the sub-map with the most matching sub-maps based on the reflective column identification codes in the sub-map, performing multilateral localization in that sub-map to obtain the robot localization result.

6. The robot positioning system based on a simplified reflective pillar map as described in claim 5, characterized in that, In the site plan construction module, the construction of the site plan includes: Step 1: For any subgraph, determine if it is the first subgraph. If so, save it to the overall graph list and set the global pose flag of the subgraph to true, then proceed to Step 4; otherwise, proceed to Step 2. Step 2: Determine whether there are constraints on the subgraph in the overall diagram list and whether global coordinates can be obtained based on polygonal positioning. If not, save this subgraph to the overall diagram list and set the global pose flag of the subgraph to false. If yes, proceed to Step 3. Step 3: Save the constraint relationship between this sub-graph and the sub-graphs in the overall drawing list, determine the identification code of the reflector, save this sub-graph to the overall drawing list, and set the global pose flag of this sub-graph to true, then proceed to step 4; Step 4: Construct least squares relationships based on constraints, solve for the reflector pillars and sub-map world coordinates that minimize error, save the overall map, assign an identification code to each reflector pillar, and proceed to Step 5: Step 5: Determine if the global pose flag of the sub-image is true. If it is, discard the sub-image. If not, record the identification code of the sub-image, perform polygonal positioning, and mark the global coordinates of the reflector in the sub-image and the corresponding identification code in the global coordinate map.

7. A computer-readable storage medium having a program stored thereon, characterized in that, When executed by the processor, the program implements the steps in the robot localization method based on a simplified reflective pillar map as described in any one of claims 1-4.

8. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the robot localization method based on a simplified reflective pillar map as described in any one of claims 1-4.

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