Tunnel ultra-wideband positioning anchor point deployment method and system based on laser radar mapping

Abstract

The invention belongs to the UWB positioning technology, and particularly provides a tunnel ultra-wideband positioning anchor point deployment method and system based on laser radar mapping, and the method comprises the steps: calculating the curvature of each point in a point cloud through employing tunnel point cloud data generated by a laser radar, reserving high-curvature points through setting a threshold value, and enabling the high-curvature points to form a high-curvature point set; removing redundant points in the high curvature point set by using a farthest point sampling algorithm, clustering the retained feature points by using a clustering algorithm, and deploying the centroid of each cluster after clustering as an initial anchor point; setting a plurality of virtual UWB test points, and calculating a weighted positioning precision factor of each test point; and optimizing the initial anchor point deployment by using an optimization algorithm to obtain optimized anchor point deployment. By combining feature extraction, clustering analysis and an optimization algorithm, a high-quality initial anchor point deployment scheme can be generated, and the positions of the anchor points can be further optimized to improve the precision and robustness of a positioning system, so that efficient and reliable UWB anchor point deployment is realized.

Description

Technical Field

[0001] The present invention relates to UWB positioning technology, and in particular to a method and system for deploying tunnel ultra-wideband positioning anchor points based on laser radar mapping. Background Art

[0002] When mobile robots perform tasks in corridor environments, they cannot rely on satellite positioning and navigation systems for precise positioning, as they do outdoors, due to the limitations of underground or indoor environments. Satellite signals in enclosed spaces like tunnels and mines are severely blocked by obstacles such as walls and rock formations, resulting in signal attenuation or even complete interruption, making them incapable of meeting positioning requirements. Therefore, ultra-wideband (UWB) technology has been widely adopted in such scenarios as a highly effective alternative. UWB technology, with its unique signal characteristics, enables high-precision positioning services in complex environments, making it a key technical solution for addressing positioning challenges in corridor environments. UWB, a wireless communication technology based on ultrashort pulse signals, has been widely used in indoor positioning due to its advantages such as wide bandwidth, low power consumption, and high temporal resolution. Particularly in underground environments like tunnels and mines, UWB technology can accurately calculate the distance between devices by measuring the signal's time of flight (TOF), providing sub-meter positioning accuracy. Compared to traditional wireless technologies, it offers superior interference immunity and signal penetration. The core of a UWB positioning system lies in the distance measurement and position calculation between UWB anchor points on fixed devices and UWB tags on mobile devices. The deployment location of anchor points directly determines the system's positioning accuracy and coverage. Therefore, optimizing the deployment of UWB anchor points in specific environments has become a key technical issue in improving positioning performance.

[0003] Tunnels, as a typical corridor environment, possess unique physical characteristics, placing higher demands on the application of UWB positioning technology. Tunnels typically feature long, narrow passageways, often incorporating a variety of features, including straight sections, curved sections, branching intersections, and varying elevations. This complex three-dimensional spatial structure results in a UWB signal propagation path that is no longer singular but instead subject to multiple influences from walls, metal structures, and other obstacles within the tunnel. Signals may experience reflection, scattering, and refraction during propagation, resulting in the signal received at the receiver containing echoes from multiple propagation paths. This phenomenon is known as multipath. Multipath significantly increases ranging errors, particularly over long distances or in areas with significant geometric changes, such as corners, bifurcations, and tunnel junctions. For example, in a straight section, a signal may propagate along the primary path, but at corners, reflected signals may overlap with the direct signal, causing ranging results to deviate from the true value. Furthermore, dynamic adjustments to equipment layout during tunnel construction and the frequent passage of vehicles further alter the signal propagation environment, increasing uncertainty. This dynamic change requires that UWB anchor point deployment not only cover the main positioning area, but also have sufficient adaptability to cope with changes in environmental conditions and ensure the stability and reliability of the positioning system under various working conditions.

[0004] To fully leverage the advantages of UWB positioning technology in tunnel environments, anchor point deployment requires special attention to signal coverage and multipath suppression. By rationally deploying ultra-wideband anchor points within the corridor, signal coverage can be significantly improved, reducing positioning blind spots caused by occlusion or reflection, thereby maximizing positioning accuracy within limited resources. This deployment strategy, based on environmental characteristics, can effectively address the issues of insufficient signal coverage and low reliability, providing reliable support for mobile robots' navigation, path planning, and task execution in tunnel environments. Traditional UWB anchor point deployment methods face significant technical challenges in complex tunnel environments, making it difficult to meet the high requirements for positioning accuracy and stability in actual engineering applications.

[0005] The application of UWB technology in tunnels stems from its high-precision ranging capabilities and adaptability to complex environments. The long and narrow structure, multipath effects, and dynamic nature of tunnels require an anchor point deployment solution that fully utilizes environmental information and improves positioning performance through a strategic layout. Proper anchor point deployment not only enhances signal coverage and reduces blind spots, but also optimizes system efficiency while reducing hardware costs, providing technical support for positioning needs in tunnels.

[0006] To properly deploy ultra-wideband positioning anchor points, the existing solutions are: (1) The Chinese invention patent with announcement number CN108280872A is entitled “UWB positioning base station deployment scheme and evaluation method based on substation three-dimensional model”. In the accurately reconstructed substation three-dimensional model scene, by simulating the movement of the target object and the line-of-sight connection of the UWB base station in the model, the number of base stations directly connected to the target object is counted, the optimal base station deployment scheme is screened, and its effect is evaluated by area ratio. First, it is necessary to set the base station position and parameters according to the substation drawing, build the initial scene model, set the base station and the target object in the three-dimensional model, scan the target object row by row, and record the line-of-sight connection between the base station and the target object. The top n base stations with the most direct connections are counted as the preferred scheme, and the area covered by more than five base stations is marked. Finally, the area ratio of the area that is not connected to at least five base stations is calculated and compared with the set threshold, and the number of base stations is adjusted until the requirements are met. The purpose is to ensure that the deployment scheme is consistent with the actual effect through accurate three-dimensional model simulation, and to optimize the number and location of base stations to improve positioning performance.

[0007] The disadvantages of this method are: it relies on scene drawings and constructs a three-dimensional model of the substation based on them, and the initial modeling process is complex and time-consuming. Since the deployment plan is completely based on static model simulation, if the location of the equipment in the actual environment changes and the model is not updated in time, the simulation results may not accurately reflect the actual scene, resulting in poor deployment effect after optimization. Secondly, this method requires the collection of a large amount of data and multiple statistical analyses. Especially in large-scale scenes, the amount of data and computing requirements are huge, which consumes a lot of time and computing resources, significantly increasing the deployment cost and time overhead. In addition, this method has weak adaptability to environmental changes. When the number or location of base stations needs to be adjusted in the scene, the entire model must be rescanned and the analysis process repeated. This inefficient adjustment method limits its flexibility and practicality in actual applications.

[0008] (2) Announcement No. CN115835227A provides a base station deployment method based on UWB positioning. The method first numbers four UWB base stations in sequence as UWB base station 1 to UWB base station 4, and places them in a counterclockwise order to form an electronic fence. The inside of the fence is the working area. With UWB base station 1 as the coordinate origin, the direction from base station 1 to base station 2 is defined as the X axis, and the direction from base station 1 to base station 4 is defined as the Y axis, so as to quickly establish an XY coordinate system. This method does not require external measurement equipment or manual establishment of a coordinate system. Position calculation can be completed only through the ranging data between base stations, which significantly reduces deployment time and human intervention, thereby improving efficiency while ensuring the stability and accuracy of the positioning system.

[0009] This method has shortcomings: First, it requires the four UWB base stations to be placed in a strict counterclockwise order to form an electronic fence. This fixed layout is less adaptable to real-world environments. In complex scenarios, terrain, obstacles, or spatial constraints may not meet this requirement, making deployment difficult or limiting coverage. Second, this method calculates coordinates solely based on time-of-flight (TOF) ranging data between base stations, failing to account for multipath and non-line-of-sight (NLOS) interference. In areas with significant wall reflection or obstruction, ranging errors can significantly increase, affecting the accuracy of coordinate calculations and reducing the reliability of the positioning system. Furthermore, this method assumes the base station deployment area is a planar XY coordinate system, ignoring three-dimensional height variations, limiting its applicability in three-dimensional environments. Third, this method relies on manual placement of base stations and coordinate calculations based on intersections. This calculation process is complex and susceptible to cumulative ranging errors, resulting in unstable deployment results. Finally, this method is only applicable to a fixed number of four base stations and lacks a mechanism for flexibly adjusting the number of base stations. In scenarios requiring wider coverage or higher accuracy, performance cannot be optimized by adding base stations, resulting in limited scalability. These shortcomings greatly limit the practical application of this method in complex or dynamic environments.

[0010] In summary, while many current studies attempt to optimize anchor point locations through algorithms, they generally fail to fully utilize prior information about the tunnel environment. For example, traditional optimization algorithms, when processing tunnel point cloud data, often focus solely on simple distance or coverage calculations, while ignoring the influence of key spatial features such as tunnel curvature and obstacle distribution. This underutilization of information makes the optimization process prone to local optimal solutions, making it difficult to find a globally optimal anchor point layout. Furthermore, existing methods suffer from low computational efficiency, making real-time performance difficult to guarantee, particularly in large-scale tunnel scenarios. This limits their widespread application in practical engineering projects. Summary of the Invention

[0011] The technical problem to be solved by the present invention is to provide a method and system for deploying tunnel ultra-wideband positioning anchor points based on lidar mapping, overcome the technical limitations of traditional UWB deployment methods in tunnel environments, and improve UWB positioning performance by introducing lidar mapping technology.

[0012] In order to solve the above technical problems, the technical solution adopted by the present invention is: A method for deploying ultra-wideband positioning anchor points in tunnels based on lidar mapping includes the following steps: Step 1: Use a mobile robot equipped with an onboard computer and lidar, run the lidar mapping system on the mobile robot, control the mobile robot to move in the area where anchor points need to be deployed, and obtain environmental point cloud data; Step 2: Downsample the environmental point cloud data and calculate the curvature of each point in the point cloud. Set a threshold to retain high curvature points, which form a high curvature point set. Step 3: Use the farthest point sampling algorithm to remove redundant points in the high curvature point set, use the clustering algorithm to cluster the retained feature points, and use the centroid of each cluster as the initial anchor point deployment; Step 4: Set up multiple UWB test points and calculate the weighted positioning precision factor of each test point; Step 5: Set the optimization goal and use the optimization algorithm to optimize the initial anchor point deployment to obtain the optimized anchor point deployment.

[0013] In the preferred solution, in step 2, downsampling the environmental point cloud data is performed as a preprocessing of the collected original point cloud data: the original point cloud is sparsely processed using voxel grid filtering, and then outliers are removed through statistical filtering to obtain sparse and denoised point cloud data.

[0014] In the preferred solution, in step 2, for each point in the point cloud , the curvature is calculated by analyzing the eigenvalues ​​of its local neighborhood covariance matrix.

[0015] In a preferred solution, the curvature calculation steps of each point in the point cloud are: For point , using KD tree for fast retrieval -Nearest neighbor points, forming a neighborhood point set , calculate the neighborhood point set The covariance matrix of , the calculation formula is: ; in, Neighborhood point set The total number of midpoints, Neighborhood point set The center of mass of is obtained by averaging the coordinates of all points. Neighborhood point set The point in Pair covariance matrix Perform eigenvalue decomposition to obtain eigenvalues , and sorted in ascending order as ,in, Represents the normal variance, the smaller its value, the flatter the surface; point Curvature Calculated by the following formula: .

[0016] In a preferred solution, in step 3, the operation steps of using the farthest point sampling algorithm to remove redundant points in the high curvature point concentration are as follows: 1) From the high curvature point set Randomly select an initial point As the first feature point, the selected point set ; 2) Calculation set Each point in To Selected Set Any point Minimum distance: calculate To Selected Set The minimum distance between any points in is expressed as: ; in, Yes To Selected Set Any point The minimum distance, and represents the Euclidean distance; 3) Select new points and merge them into the selected point set In the new set of selected points : Select a point , so that the minimum distance calculated in step 2) is maximized, and the expression is: ; Will Add to Selected Point Set , merge with the points in the original selected point set, and update : ; Repeat the above steps 2) to 3) until the high curvature point set Select the required number of feature points.

[0017] In a preferred solution, in step 3, the clustering algorithm is DBSCAN algorithm; in step 5, the optimization algorithm is adaptive temperature simulated annealing algorithm, particle swarm optimization algorithm, genetic algorithm or differential evolution algorithm.

[0018] In a preferred solution, in step 4, calculating the weighted positioning precision factor of each test point is calculating the weighted positioning precision factor between each UWB test point and its two nearest anchor points.

[0019] In the preferred solution, the decomposed WPDOP calculation method is used to calculate the weighted positioning precision factor between each UWB test point and its two nearest anchor points. The operation steps are as follows: Assume the target UWB test point location is , No. The anchor point position is , then the target UWB test point is The distance between anchor points for: ; extract The components of the matrix, The component expressions of the matrix are: ; ; ; Where: The matrix represents the sensitivity of the target UWB test point position error to each measurement; the two anchor point positions adjacent to the target UWB test point are 、 ; and are the distances between the target UWB test point and the two adjacent anchor points; Define the weight matrix , the expression is: ; in, and are the variances of the ranging errors between the target UWB test point and the two adjacent anchor points; The single-dimensional WPDOP of the X, Y, and Z axes is calculated as follows: ; ; ; Finally, WPDOP is obtained by combining the results of three directions: .

[0020] In a preferred solution, in step 5, the optimization goal is to minimize the average WPDOP of all UWB test points.

[0021] The present invention also provides a system for deploying ultra-wideband positioning anchor points in tunnels based on lidar mapping, which is used to execute the above-mentioned method for deploying ultra-wideband positioning anchor points in tunnels based on lidar mapping, comprising: LiDAR environment modeling module: collects and processes corridor 3D point cloud data; Initial anchor point generation module: Generates the initial anchor point deployment plan based on point cloud curvature analysis and clustering algorithm; Anchor point optimization module: Optimizes anchor point layout by decomposing WPDOP calculation and improving simulated annealing algorithm.

[0022] The present invention provides a method and system for deploying ultra-wideband positioning anchor points in tunnels based on lidar mapping, which has the following beneficial effects: 1. This invention combines lidar-based map construction with UWB deployment to accurately capture the geometric characteristics of corridors through precise environmental modeling. Using environmental point cloud data, corridor point cloud curvature analysis is used to identify environmental features. A farthest point sampling algorithm is used for high-curvature points exceeding a threshold. Uniformly distributed feature points are selected from the high-curvature point set to ensure that there is no localized clustering within the coverage area. A density clustering algorithm is then used to generate an optimal initial anchor point deployment plan, ensuring that anchor point deployment is strongly correlated with environmental geometric features. This process significantly reduces blind spots and lowers hardware costs, thereby improving positioning reliability and deployment efficiency in complex corridor scenarios.

[0023] 2. Use lidar to build a point cloud map of the environment. First, use covariance matrix decomposition to extract eigenvalues, calculate the curvature of the point cloud data, and filter out high curvature areas. These areas usually represent geometric mutation locations such as edges and corners. By screening high curvature areas, key environmental features can be quickly captured. Subsequently, the farthest point sampling algorithm is used to select evenly distributed feature points from the high curvature point set to ensure that there is no local aggregation in the coverage area; further combined with the DBSCAN density clustering algorithm, the dense area is divided into clusters and redundant points are removed, and finally the centroid of each cluster is used as the initial anchor point position. This method achieves efficient deployment of anchor points in complex corridor environments through the curvature calculation formula, threshold screening, and the joint optimization process of FPS and DBSCAN, covering geometrically significant areas while avoiding redundancy, providing a high-quality initial solution for subsequent optimization.

[0024] 3. In view of the limitations of traditional UWB anchor deployment technology in complex environments, the present invention introduces a WPDOP calculation method suitable for corridor environments, and combines it with an adaptive temperature simulated annealing algorithm to optimize the anchor layout, thereby improving positioning accuracy. First, in view of the problem that the traditional WPDOP algorithm is prone to numerical instability under the nearly collinear configuration of anchor points, the present invention innovatively decomposes the direction vector into the X / Y / Z axes, calculates the single-dimensional WPDOP of each axis separately, and performs a three-dimensional combination. By eliminating the matrix singularity caused by collinearity, the algorithm is ensured to converge stably in complex scenarios such as long straight corridors, effectively avoiding the reduction in positioning accuracy caused by geometric configuration defects. Secondly, in view of the defects of traditional optimization algorithms that are prone to falling into local optimality and slow convergence speed, the adaptive temperature simulated annealing algorithm introduced in the present invention adopts an adaptive temperature attenuation strategy, dynamically adjusts the temperature parameters according to the error change, accelerates the global search efficiency, balances the search accuracy and efficiency, and improves the hit rate of the global optimal solution while reducing the number of iterations. It significantly improves the geometric optimization quality of anchor point layout and enhances the adaptability of the algorithm in complex environments, ultimately improving the accuracy and robustness of the UWB positioning system. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The present invention will be further described below with reference to the accompanying drawings and embodiments: Figure 1 Flowchart of the present invention; Figure 2 The actual tunnel scene in the embodiment and the corresponding computer simulation modeling diagram; Figure 3 Schematic diagram of the generated initial anchor point deployment; Figure 4 Schematic diagram of anchor point deployment after optimization. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0027] Example 1: This invention provides a method for deploying ultra-wideband positioning anchor points in tunnels based on lidar mapping. First, the method uses lidar to generate a high-precision tunnel point cloud model and combines it with curvature analysis to extract key environmental features, overcoming the existing technology's underutilization of geometric features. Simultaneous Localization and Mapping (SLAM) technology is used to collect three-dimensional tunnel point cloud data in real time, generating an environmental model that includes details such as straight segments, corners, and intersections. Based on this, the method calculates the eigenvalues ​​of the covariance matrix of each point in the point cloud, analyzes local curvature variations, and identifies high-curvature areas, such as corners and bifurcations, as potential anchor point deployment locations. Subsequently, the point cloud is downsampled using the farthest point sampling (FPS) method to preserve key geometric features. Feature point sets are then generated using the density-based spatial clustering of applications with noise (DBSCAN) algorithm, and the center point of each feature point set is used to form the initial anchor point layout. This adaptive deployment based on environmental features ensures that anchor points preferentially cover complex areas.

[0028] Secondly, by introducing a decomposed WPDOP calculation method as an evaluation criterion, it comprehensively assesses the geometric distribution of anchor points and the impact of multipath effects, addressing the lack of evaluation criteria for specialized environments in existing technologies. Traditional WPDOP calculations in tunnels are prone to matrix singularities due to the near-linear arrangement of anchor points. This new method projects the direction vector onto the X, Y, and Z axes, calculates the single-dimensional WPDOP for each axis separately, and ultimately combines them to form a total WPDOP. This method not only avoids numerical instabilities and enhances robustness to complex propagation conditions, but also overcomes the limitations of existing technologies in handling signal interference.

[0029] Finally, this invention uses a simulated annealing algorithm with adaptive temperature decay to optimize anchor point locations, improving deployment efficiency in large-scale scenarios and addressing the inefficiencies of existing optimization techniques. The algorithm dynamically adjusts the cooling rate based on error fluctuations, minimizing WPDOP to ensure optimal coverage and accuracy. This provides a precise, efficient, and adaptable anchor point deployment solution for tunnel positioning systems, meeting the high-precision positioning requirements of tunnel projects.

[0030] like Figure 1 As shown, the specific steps include: Step 1: Use a mobile robot equipped with an onboard computer and lidar. Run a lidar mapping system, such as LIO-SAM, on the mobile robot. Control the mobile robot to move in the area where anchor points need to be deployed. The path needs to include the entire tunnel environment to obtain environmental point cloud data.

[0031] The mobile robot is equipped with an onboard computer, which uses a laser inertial odometry system and a mapping system that integrates a lidar and inertial measurement unit (IMU). The robot is controlled to move around in areas where anchor points are required, collecting 3D point cloud data in real time to achieve 3D point cloud modeling of the complex tunnel environment. The resulting complete point cloud data is then stored on a computer to create a global map in PCD point cloud format.

[0032] Step 2: Downsample the environmental point cloud data and calculate the curvature of each point in the point cloud. High curvature points are retained by setting a threshold, and the high curvature points form a high curvature point set.

[0033] The collected original point cloud data needs to be preprocessed to reduce the computational complexity and retain key features. Therefore, the saved point cloud is preprocessed and the original point cloud is sparsely processed using voxel grid filtering to reduce the amount of point cloud data while retaining the main geometric features. Subsequently, outliers are removed through statistical filtering to obtain sparse and denoised point cloud data.

[0034] Based on the preprocessed point cloud data, the environmental geometric features are extracted through curvature analysis, and the initial anchor point deployment plan is generated by combining the farthest point sampling and density clustering algorithm to ensure that the anchor points are evenly distributed and cover key areas.

[0035] In this embodiment, for each point in the point cloud , the curvature is calculated by analyzing the eigenvalues ​​of its local neighborhood covariance matrix. Alternatively, curvature tensor analysis can be used, or a deep learning-based curvature estimation network can be employed, and the curvature threshold can be replaced with an adaptive threshold algorithm based on local point density.

[0036] The steps for calculating the curvature of each point in the point cloud are: For point , using KD tree for fast retrieval -Nearest neighbor points, forming a neighborhood point set , calculate the neighborhood point set The covariance matrix of , the calculation formula is: ; in, Neighborhood point set The total number of midpoints, Neighborhood point set The center of mass of is obtained by averaging the coordinates of all points. Neighborhood point set The point in Pair covariance matrix Perform eigenvalue decomposition to obtain eigenvalues , and sorted in ascending order as ,in, Represents the normal variance, the smaller its value, the flatter the surface; point Curvature Calculated by the following formula: .

[0037] To screen geometrically critical areas, such as edges or corners, a dynamic curvature threshold is set to extract high-curvature points. These areas are particularly important for localization coverage. Compared to deep learning semantic segmentation methods, curvature analysis does not require complex reasoning, significantly reducing computational costs.

[0038] Step 3: Use the farthest point sampling algorithm to remove redundant points in the high curvature point set, use the density clustering algorithm to cluster the retained feature points, and use the centroid of each cluster after clustering as the initial anchor point deployment.

[0039] The density clustering algorithm uses the DBSCAN algorithm. Other clustering methods can be used to generate the initial anchor point deployment according to the requirements of different scenarios.

[0040] In order to further extract representative feature points, the farthest point sampling (FPS) algorithm is applied to the high curvature point set. ,The steps of using the farthest point sampling algorithm to remove redundant points in the high curvature point concentration are as follows: 1) From the high curvature point set Randomly select an initial point As the first feature point, the selected point set ; 2) Calculation set Each point in To Selected Set Any point Minimum distance: calculate To Selected Set The minimum distance between any points in is expressed as: ; in, Yes To Selected Set Any point The minimum distance, and represents the Euclidean distance; 3) Select new points and merge them into the selected point set In the new set of selected points : Select a point , so that the minimum distance calculated in step 2) is maximized, and the expression is: ; Will Add to Selected Point Set , merge with the points in the original selected point set, and update : ; Repeat the above steps 2) to 3) until the high curvature point set Select the required number of feature points.

[0041] The FPS algorithm ensures that the feature points are evenly distributed and fully cover the point cloud geometry. Then, the density-based spatial clustering algorithm (DBSCAN) is applied to Clustering is performed to remove redundant or overcrowded points. DBSCAN parameters were optimized experimentally to adapt to the point cloud density of different tunnel scenarios. After clustering, the centroid of each cluster—the average of the coordinates of the points within the cluster—is selected as the initial anchor point. This method improves anchor point placement quality, reduces computational overhead, and enhances robustness to noise. Ultimately, all cluster centroids constitute the initial anchor point placement plan.

[0042] Step 4: Set up multiple UWB test points and calculate the weighted positioning precision dilution (WPDOP) of each test point.

[0043] WPDOP is a key metric for measuring positioning system accuracy, reflecting the geometric quality of the spatial distribution of anchor points. A smaller WPDOP value indicates a more optimal positioning system geometry and higher positioning accuracy. Minimizing WPDOP significantly improves positioning system performance, providing more accurate and reliable positioning results.

[0044] To achieve this, the matrix Encode the relative distance between the UWB test point and the anchor point. The matrix represents the sensitivity of the target UWB test point position error to each measurement. Assume that the target UWB test point position is , No. The anchor point position is , then the UWB test point and the The distance between anchor points for: ; The matrix The row contains three elements, indicating the sensitivity of the UWB test point position deviation to measurement error: ; In addition, define the weight matrix , a diagonal matrix, used to reflect the error variance of different measurements. In the tunnel environment, to reduce the computational complexity and adapt to its characteristics, only the two anchor points closest to the UWB test point are considered. The matrix is ​​represented as: ; in, It is The variance of the distance measurement error of each anchor point is derived from the established UWB mathematical model.

[0045] To characterize the interaction between the geometric distribution of anchor points and measurement error, the matrix is ​​defined as , the trace of its inverse matrix is ​​directly related to WPDOP. The calculation formula of WPDOP is: ; However, when using UWB positioning in tunnel environments, the traditional WPDOP calculation method has obvious limitations. The geometric constraints of tunnel corridors often lead to an approximately linear arrangement of anchor points and UWB tags on mobile devices, and the collinearity of direction vectors makes the The matrix rank is reduced, which leads to singularity problems and makes it impossible to calculate the inverse matrix. This instability interferes with the optimization process, especially in algorithms that rely on continuous objective functions, such as simulated annealing.

[0046] Therefore, a decomposed WPDOP calculation method suitable for tunnel environment is proposed. The direction vector is projected onto the X, Y, and Z axes to extract the Components of the matrix: ; ; ; The single-dimensional WPDOP of each axis is calculated as follows: ; ; ; Finally, WPDOP is obtained by combining the results of three directions:

[0047] This method calculates the WPDOP for each axis separately, fully reflecting the different contributions of the primary and secondary directions to positioning accuracy. This method is particularly well-suited for the geometric characteristics of long and narrow corridors. It also effectively avoids matrix singularities in collinear configurations and maintains numerical stability even when the anchor points are nearly collinear. Consequently, this method exhibits improved convergence and robustness when optimizing UWB deployments in tunnel environments.

[0048] Step 5: Set the optimization goal to minimize the average WPDOP across all UWB test points. Use an adaptive temperature simulated annealing algorithm to optimize the initial anchor point deployment, adjusting parameters such as the initial temperature and descent rate based on environmental factors. This process continues until the optimization goal is achieved or the termination temperature is reached. The optimized anchor point deployment coordinates are then output, resulting in the optimized anchor point deployment.

[0049] By decomposing the WPDOP calculation and applying an improved simulated annealing algorithm, the initial anchor point layout is globally optimized, addressing the numerical instability issues inherent in traditional methods in corridor environments due to anchor point collinearity. Considering the significant impact of the geometric characteristics of tunnel environments on positioning system performance, an adaptive temperature decay strategy is introduced into the optimization process. This strategy dynamically adjusts the temperature decay rate based on the solution improvement in the current iteration, effectively balancing the needs of global search and local optimization.

[0050] Specifically, if the error of the current solution changes little (i.e., the positioning error decreases slowly), it indicates that the solution may be close to a local optimum or in a flat region. In this case, the algorithm should maintain a slower cooling rate to conduct a more refined local search and avoid premature convergence. Conversely, if the error decreases significantly, indicating that the optimization is progressing smoothly, the cooling rate can be accelerated to quickly approach the global optimal solution. The introduction of an adaptive temperature decay strategy improves the algorithm's convergence efficiency while reducing the risk of falling into a local optimum, thereby improving the accuracy and stability of anchor deployment.

[0051] The simulated annealing algorithm can be replaced by particle swarm optimization, genetic algorithm, or differential evolution to achieve global deployment optimization.

[0052] Example 2: The present invention has been verified by computer simulation to prove the feasibility and effectiveness of its optimized deployment of UWB anchor points in a tunnel environment. The specific process is as follows.

[0053] This experiment evaluated the proposed method on a publicly available simulated environment dataset, selecting a mine tunnel scenario approximately 150 meters long. This scenario simulates positioning scenarios in a complex underground environment and provides a standard testbed for ultra-wideband positioning anchor deployment methods in tunnels based on lidar mapping. A 3D point cloud map of the environment was constructed using the LIO-SAM system. The actual tunnel scene and the generated point cloud map are shown in Figure 2. The point cloud primarily depicts a long, straight tunnel corridor with a lateral intersection in the middle leading to a smaller branch tunnel. Because the robot did not enter this intersection during data collection, the generated point cloud map primarily reflects the main tunnel, and the branch tunnel environment is not fully captured. The lack of point cloud data within the branch tunnel limits subsequent localization and path planning optimization to the area of ​​the main tunnel that the robot has already explored and mapped.

[0054] Experimental results show the initial anchor point generation results, as shown in Figure 3. The gray points in the figure represent the downsampled point cloud data. Despite the lower density, they still retain the main geometric features of the tunnel and its surroundings. The blue cone structure represents the anchor point locations automatically selected at this stage, providing a basis for subsequent optimization.

[0055] Five UWB tags were generated in the environment. The anchor point deployment was iteratively optimized by evaluating the WPDOP value between each tag and its two nearest anchor points. The optimized anchor point locations are shown in Figure 4. They are primarily distributed along the road, with moderate spacing, effectively covering most of the road area.

[0056] This distribution fully utilizes the geometric characteristics of the point cloud environment, reducing the likelihood of anchor points becoming overly dense or deviating from the valid area. Using the improved simulated annealing algorithm, the overall WPDOP value of the system fluctuated slightly initially, but stabilized after approximately 300 iterations, ultimately reaching a relatively stable WPDOP value. The average WPDOP after optimization was 0.507, a decrease of approximately 38.5% from the 0.824 of the traditional empirical deployment and approximately 35.6% from the 0.787 of the initial deployment, significantly reducing the impact of geometric dilution on positioning accuracy. The comparative results are shown in Table 1.

[0057]

[0058] In summary, experimental and simulation validation demonstrates that the proposed method significantly reduces the system's WPDOP by over 30%. This method provides a practical solution for optimizing UWB anchor point deployment in mine tunnels and corridors, and a scalable and reusable implementation framework for enhancing indoor positioning systems in geometrically constrained environments.

[0059] Example 3: The present invention also provides a system for deploying ultra-wideband positioning anchor points in tunnels based on lidar mapping, which is used to execute the method for deploying ultra-wideband positioning anchor points in tunnels based on lidar mapping described in Example 1, including: LiDAR environment modeling module: collects and processes corridor 3D point cloud data; Initial anchor point generation module: Generates the initial anchor point deployment plan based on point cloud curvature analysis and clustering algorithm; Anchor point optimization module: Optimizes anchor point layout by decomposing WPDOP calculation and improving simulated annealing algorithm.

[0060] It will be easily understood by those skilled in the art that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for deploying ultra-wideband positioning anchor points in tunnels based on lidar mapping, characterized in that: The following steps are involved: Step 1: Use a mobile robot equipped with an onboard computer and lidar, run the lidar mapping system on the mobile robot, control the mobile robot to move in the area where anchor points need to be deployed, and obtain environmental point cloud data; Step 2: Downsample the environmental point cloud data and calculate the curvature of each point in the point cloud. Set a threshold to retain high curvature points, which form a high curvature point set. Step 3: Use the farthest point sampling algorithm to remove redundant points in the high curvature point set, use the clustering algorithm to cluster the retained feature points, and use the centroid of each cluster as the initial anchor point deployment; Step 4: Set up multiple UWB test points and calculate the weighted positioning precision factor of each test point; Step 5: Set the optimization goal and use the optimization algorithm to optimize the initial anchor point deployment to obtain the optimized anchor point deployment.

2. The method for deploying ultra-wideband positioning anchor points in tunnels based on lidar mapping according to claim 1, characterized in that: In step 2, downsampling the environmental point cloud data is performed as a preprocessing of the collected original point cloud data: the original point cloud is sparsely processed using voxel grid filtering, and then outliers are removed through statistical filtering to obtain sparse and denoised point cloud data.

3. The method for deploying ultra-wideband positioning anchor points in tunnels based on lidar mapping according to claim 1, characterized in that: In step 2, for each point in the point cloud , the curvature is calculated by analyzing the eigenvalues ​​of its local neighborhood covariance matrix.

4. The method for deploying ultra-wideband positioning anchor points in tunnels based on lidar mapping according to claim 3, characterized in that: The steps for calculating the curvature of each point in the point cloud are: For point , using KD tree for fast retrieval -Nearest neighbor points, forming a neighborhood point set , calculate the neighborhood point set The covariance matrix of , the calculation formula is: ； in, Neighborhood point set The total number of midpoints, Neighborhood point set The center of mass of is obtained by averaging the coordinates of all points. Neighborhood point set The point in Covariance matrix Perform eigenvalue decomposition to obtain eigenvalues , and sorted in ascending order as ,in, Represents the normal variance, the smaller its value, the flatter the surface; point Curvature Calculated by the following formula: 。 5. The method for deploying ultra-wideband positioning anchor points in tunnels based on lidar mapping according to claim 1, characterized in that: In step 3, the operation steps of using the farthest point sampling algorithm to remove redundant points in the high curvature point set are as follows: 1) From the high curvature point set Randomly select an initial point As the first feature point, the selected point set ; 2) Calculation set Each point in To Selected Set Any point Minimum distance: calculate To Selected Set The minimum distance between any points in is expressed as: ； in, Yes To Selected Set Any point The minimum distance, and represents the Euclidean distance; 3) Select new points and merge them into the selected point set In the new set of selected points : Select a point , so that the minimum distance calculated in step 2) is maximized, and the expression is: ； Will Add to Selected Point Set , merge with the points in the original selected point set, and update : ； Repeat the above steps 2) to 3) until the high curvature point set Select the required number of feature points.

6. The method for deploying ultra-wideband positioning anchor points in tunnels based on lidar mapping according to claim 1, characterized in that: In step 3, the clustering algorithm is selected from the DBSCAN algorithm; in step 5, the optimization algorithm is selected from the adaptive temperature simulated annealing algorithm, the particle swarm optimization algorithm, the genetic algorithm, or the differential evolution algorithm.

7. The method for deploying ultra-wideband positioning anchor points in tunnels based on lidar mapping according to claim 1, characterized in that: In step 4, calculating the weighted positioning precision factor of each test point is calculating the weighted positioning precision factor between each UWB test point and its two nearest anchor points.

8. The method for deploying ultra-wideband positioning anchor points in tunnels based on lidar mapping according to claim 7, characterized in that: The decomposed WPDOP calculation method used is to calculate the weighted positioning precision factor between each UWB test point and its two nearest anchor points. The operation steps are as follows: Assume the target UWB test point location is , No. The anchor point position is , then the target UWB test point is The distance between anchor points for: ； extract The components of the matrix, The component expressions of the matrix are: ； ； ； Where: The matrix represents the sensitivity of the target UWB test point position error to each measurement; The two anchor points adjacent to the target UWB test point are 、 ; and are the distances between the target UWB test point and the two adjacent anchor points; Define the weight matrix , the expression is: ； in, and are the variances of the ranging errors between the target UWB test point and the two adjacent anchor points; The single-dimensional WPDOP of the X, Y, and Z axes is calculated as follows: ； ； ； Finally, WPDOP is obtained by combining the results of three directions: 。 9. The method for deploying ultra-wideband positioning anchor points in tunnels based on lidar mapping according to claim 1, characterized in that: In step 5, the optimization goal is to minimize the average WPDOP of all UWB test points.

10. A tunnel ultra-wideband positioning anchor point deployment system based on lidar mapping, characterized in that: The method for deploying ultra-wideband positioning anchor points in a tunnel based on lidar mapping according to any one of claims 1 to 9 comprises: LiDAR environment modeling module: collects and processes corridor 3D point cloud data; Initial anchor point generation module: Generates the initial anchor point deployment plan based on point cloud curvature analysis and clustering algorithm; Anchor point optimization module: Optimizes anchor point layout by decomposing WPDOP calculation and improving simulated annealing algorithm.

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