Method for evaluating indoor laser radar scanning mapping precision

Abstract

The invention discloses a method for evaluating indoor laser radar scanning mapping precision, and aims to solve the problems that an existing evaluation technology depends on an expensive true value system and precision evaluation is difficult in a large scene. According to the method, complex equipment is not needed, and only several groups of wall surfaces with good parallelism need to be selected in an indoor space conforming to building specifications. A series of space geometric constraints with known true values can be quickly obtained by pasting reflection markers on the wall surfaces and measuring the distance between the parallel wall surfaces by using high-precision distance measuring equipment. According to the method, the process operation is simple, the cost is extremely low, and the mapping precision is accurately evaluated by quantifying the key indexes.

Description

Technical Field

[0001] This invention relates to the field of 3D scanning technology, and more specifically to a method for evaluating the accuracy of indoor lidar scanning mapping. Background Technology

[0002] With the maturity of LiDAR technology, 3D scanning and reconstruction technology has been widely applied in the field of architecture, engineering, and construction (AEC). Among them, the construction of indoor 3D point cloud maps provides a key spatial data foundation for applications such as digital building management, BIM modeling, indoor navigation, and renovation design.

[0003] Currently, indoor scanning mainly relies on two types of equipment: handheld LiDAR and stationary LiDAR. Handheld devices typically employ SLAM (Simultaneous Localization and Mapping) technology, which incrementally builds a map by estimating its own pose (position and orientation) in real time while moving and stitching together consecutive laser frames. However, the core challenge of SLAM algorithms lies in the fact that pose estimation errors accumulate over time, leading to map distortion, overlap, or "ghosting" phenomena. To accurately assess its mapping accuracy, an optical motion capture system is usually required as a ground truth reference for pose. However, such systems are not only expensive but also require the pre-deployment of a large number of marker points, making them difficult to deploy in spacious or structurally complex indoor scenes. This significantly limits the accuracy certification and widespread application of handheld LiDAR in large-scale indoor mapping scenarios.

[0004] On the other hand, while station-based LiDAR offers high measurement accuracy at a single site, constructing large-scale maps requires stitching together scan results from multiple sites. The stitching and registration process between sites introduces errors, which accumulate and spread with the number of sites, ultimately affecting the absolute accuracy and consistency of the overall map. Currently, there is a lack of efficient and comprehensive quantitative evaluation methods for assessing the accuracy of station-based scanning results. Evaluation often relies on manual judgment or spot checks using equipment such as total stations, which is inefficient and fails to fully reflect the overall map quality.

[0005] Therefore, whether for handheld SLAM devices or gantry-based scanning solutions, the industry urgently needs an automated method for calculating the accuracy of indoor point cloud maps that does not rely on expensive truth systems, in order to effectively assess and constrain mapping errors and ensure the reliability of 3D spatial data. Summary of the Invention

[0006] The purpose of this invention is to provide a method for evaluating the accuracy of indoor lidar scanning mapping, aiming to solve the problems of existing evaluation technologies' reliance on expensive ground truth systems (such as motion capture systems) and the difficulty in accuracy assessment in large scenes.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A method for evaluating the accuracy of indoor lidar scanning mapping includes the following steps: S1. Select the scene to be evaluated with parallel walls, paste reflective markers on the walls, and use a high-precision ranging device to measure and record the actual distance between each parallel wall. S2. Use a lidar scanning device to scan the scene to be evaluated and generate a point cloud map; S3. Based on the point cloud intensity value, identify and extract the point cloud clusters corresponding to the reflection markers from the point cloud map; S4. Using each point cloud cluster as the core, gradually expand outward to extract the complete point cloud data of the wall surface. S5. Perform plane fitting on the point cloud data of each expanded wall surface to obtain the plane equation corresponding to each wall surface. S6. Based on the plane equation, calculate the measured distance between parallel wall surfaces in the point cloud map, compare the measured distance with the actual distance recorded in S1, and calculate the absolute distance error. S7. Analyze the normal vector of the fitted plane equation and calculate the direction consistency error; S8. The accuracy of lidar scanning mapping is quantitatively evaluated based on absolute distance error and direction consistency error.

[0008] Further, S1 includes: S11, selecting multiple sets of candidate parallel wall surfaces in an indoor environment where the mapping accuracy needs to be evaluated, measuring the distance between the same wall surfaces at multiple different locations using a high-precision distance measuring device and calculating the variance, and selecting wall surfaces with a variance less than a first preset threshold for evaluation, where the first preset threshold is 3mm. 2 S12. Affix reflective markers to each of the selected wall surfaces, and accurately measure and record the actual distance between each parallel wall surface.

[0009] Furthermore, the reflective marker is a reflective strip, and the size of the reflective strip is not less than 30cm. 30cm.

[0010] Further, step S3 includes: S31, filtering according to the intensity attributes of the point cloud, extracting points in the point cloud map whose intensity values ​​are higher than the second preset threshold, forming a set of high-brightness cloud, where the second preset threshold is the maximum value of the radar intensity range of the lidar; S32, performing Euclidean clustering analysis on the set of high-brightness cloud, dividing spatially adjacent high-brightness cloud into different point cloud clusters, with each point cloud cluster corresponding to a reflective marker.

[0011] Further, S4 includes: S41, calculating the directed bounding box of each point cloud cluster and using it as the initial search range; S42, based on the initial search range, iteratively expanding the region in the world coordinate system; after each iteration of expansion, searching for point clouds within the expanded region; S43, calculating the distance from the searched point cloud to the plane fitted to the corresponding initial point cloud cluster, retaining point clouds whose distance is less than a preset plane distance threshold and aggregating them into a single point cloud dataset.

[0012] Furthermore, in S42, each iteration is directed to the world coordinate system... X , Y , Z Expand by 0.3m in each direction, and iterate at least 10 times; The iterative method is as follows: S421, Obtain a pair of diagonal vertices of the directed bounding box. and , , All are point cloud coordinates of vertices, with an extended region of r = 0.3 =(1,1,1); S422, let , Calculate the expanded region after the first iteration. If point clouds exist in the expanded region after the first iteration... satisfy Then dotted clouds The point cloud after the search, For parallel condition symbols; S423, each point cloud cluster must repeat operations S421 and S422 at least 10 times; In S43, the preset plane distance threshold is 0.03m; In S43, distance The calculation formula is: ,in These are the coordinates of the point cloud after the search. a , b , c These are the fitting planes corresponding to the initial point cloud clusters. of a i , b i , c i .

[0013] Furthermore, in S5, principal component analysis is used to perform planar fitting on the point cloud data of each expanded wall surface.

[0014] Further, S6 includes: S61, in the point cloud map, manually specifying or automatically matching a pair of plane equations corresponding to the parallel wall surfaces measured in S1; S62, calculating the average distance for each pair of plane equations and using it as the measured distance; S63, calculating the root mean square error between the measured distance to each parallel wall surface and the actual distance recorded in S1 and using it as the absolute distance error.

[0015] Furthermore, S7 includes: S71, dividing the fitted plane equation into sections perpendicular to... x axis, y shaft and z The three sets of axes, when the normal vector of the fitted plane equation is in x When the components on the coordinate axes are at their maximum, the plane equation is classified as perpendicular to the coordinate axes. x A set of axes, if in y When the component on the coordinate axis is at its maximum, classify it to be perpendicular to the axis. y A set of axes, if in z When the component on the coordinate axis is at its maximum, classify it to be perpendicular to the axis. z S72. Normalize the normal vectors of all plane equations in each group to obtain three normalized normal vectors; S73. Calculate the mean, length of the average vector, and error of the three normalized normal vectors in turn to obtain the direction consistency error.

[0016] Furthermore, in S8, the smaller the absolute distance error, the higher the accuracy of the LiDAR scanning and mapping, and vice versa; the closer the consistency error is to 0, the higher the accuracy of the LiDAR scanning and mapping, and vice versa.

[0017] Compared with the prior art, the present invention has the following beneficial effects: This invention first proposes a low-cost, highly convenient, standardized scene layout scheme. Without complex equipment, it only requires selecting several sets of parallel walls within an indoor space (such as a corridor or room) that conforms to building codes. By affixing reflective markers (such as low-cost reflective strips) to the walls and using a high-precision ranging device to measure the distances between these parallel walls, a series of spatial geometric constraints (distance, parallelism) with known true values ​​can be quickly obtained. This process is simple to operate, extremely low-cost, and provides a reliable true value reference for accuracy assessment.

[0018] In terms of data processing and accuracy calculation, the core of this invention lies in its innovative extended fitting algorithm. This algorithm first efficiently filters out reflective marker point clouds based on point cloud intensity values, and then accurately extracts and fits the plane equation of the entire wall surface. Compared with traditional methods, this algorithm can more effectively overcome problems such as sparse point clouds and noise interference, significantly improving the completeness of planar feature extraction and the accuracy of equation expression.

[0019] Finally, this invention provides a comprehensive quantitative evaluation of mapping accuracy from two dimensions: planar consistency and distance error. On one hand, it assesses the directional consistency accuracy of the scanned data by calculating the angular deviation between the supposedly parallel fitted planes. On the other hand, it precisely quantifies the absolute distance error by calculating the distance between the fitted planes and comparing it with the true distance measured by the ranging device. This method not only achieves an effective evaluation of the overall geometric accuracy of the point cloud map but also provides an intuitive and reliable basis for optimizing SLAM algorithms and sensor calibration. Attached Figure Description

[0020] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0022] In the description of this invention, it should be noted that the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0023] like Figure 1 As shown, the present invention provides a method for evaluating the accuracy of indoor lidar scanning mapping, comprising the following steps: S1. Select the scene to be evaluated with parallel walls, paste reflective markers on the walls, and use a high-precision ranging device to measure and record the actual distance between each parallel wall. S2. Use a lidar scanning device to scan the scene to be evaluated and generate a point cloud map; S3. Based on the point cloud intensity value, identify and extract the point cloud clusters corresponding to the reflection markers from the point cloud map; S4. Using each point cloud cluster as the core, gradually expand outward to extract the complete point cloud data of the wall surface. S5. Perform plane fitting on the point cloud data of each expanded wall surface to obtain the plane equation corresponding to each wall surface. S6. Based on the plane equation, calculate the measured distance between parallel wall surfaces in the point cloud map, compare the measured distance with the actual distance recorded in S1, and calculate the absolute distance error. S7. Analyze the normal vector of the fitted plane equation and calculate the direction consistency error; S8. The accuracy of lidar scanning mapping is quantitatively evaluated based on absolute distance error and direction consistency error.

[0024] This invention requires no complex equipment; it only requires selecting several sets of parallel walls in an indoor space (such as a corridor or room) that conforms to building codes. By affixing low-cost reflective marking strips to the walls as feature markers and using a high-precision single-point laser rangefinder to measure the distance between these parallel walls, a series of spatial geometric constraints (distance, parallelism) with known true values ​​can be quickly obtained.

[0025] Preferably, the high-precision ranging device of the present invention is a single-point laser rangefinder, and the laser radar scanning device is a handheld SLAM laser radar scanner or a stationary laser radar scanner.

[0026] In some embodiments, S1 includes: S11, selecting multiple sets of candidate parallel wall surfaces in an indoor environment where the mapping accuracy needs to be evaluated, measuring the distance between the same wall surfaces at multiple different locations using a high-precision ranging device and calculating the variance, and selecting wall surfaces with a variance less than a first preset threshold for evaluation, where the first preset threshold is 3mm. 2 S12. Affix reflective markers to each of the selected wall surfaces, accurately measure and record the actual distance between each parallel wall surface. The selected parallel wall surfaces should meet the requirement of good parallelism, that is, flat and as parallel as possible to each other.

[0027] The following is a specific embodiment, in which multiple sets of parallel walls are selected for the following test: Ten different locations are randomly selected on one wall, and a single-point distance meter (accuracy error < 1mm) is placed perpendicular to the wall. The distance to the opposite wall is then measured, and the measurement results are recorded as follows. Through formula Calculate the variance of multiple measurement results (an unbiased estimate of the population variance). If the variance is <3mm... 2 If the two walls are parallel, then they are considered parallel. Using this method, at least one pair of walls that meet the parallelism requirement must be found in the space where the mapping accuracy needs to be evaluated. Then, for each selected wall, a highly reflective reflective strip (at least 30cm in size) is used. Mark the location on the wall with a 30cm marker. Similarly, select 10 different locations and use a single-point distance meter (accuracy error < 1mm) placed perpendicular to the wall, then measure the distance to the opposite wall and record it as [the distance is not specified in the original text]. Calculate the mean As the distance between parallel walls, all measured and calculated distances between walls are recorded as follows. .

[0028] In some embodiments, step S3 includes: S31, filtering based on the intensity attributes of the point cloud, extracting points in the point cloud map whose intensity values ​​are higher than a second preset threshold, forming a set of high-brightness cloud points, where the second preset threshold is the maximum value of the LiDAR's radar intensity range (e.g., if the LiDAR's own radar intensity value range is 0-255, then the second preset threshold is set to 255); S32, performing Euclidean clustering analysis on the set of high-brightness cloud points, dividing spatially adjacent high-brightness cloud points into different point cloud clusters, with each point cloud cluster corresponding to a reflective marker.

[0029] Since the target walls are marked with highly reflective strips, the intensity value attribute of the point cloud is used for filtering. Specifically, the complete point cloud in the point cloud map is denoted as F, and all points in F are filtered by intensity value (i.e., points with intensity values ​​higher than a second preset threshold), resulting in a set of high-intensity point clouds denoted as H. Then, the Euclidean clustering method is used to separate k wall marker point clouds, i.e., k point cloud clusters, from the high-intensity point cloud set H. To represent its set: .

[0030] In some embodiments, S4 includes: S41, calculating the directed bounding box of each point cloud cluster and using it as the initial search range; S42, based on the initial search range, iteratively expanding the region in the world coordinate system; after each iteration of expansion, searching for point clouds within the expanded region; S43, calculating the distance from the searched point cloud to the plane fitted to the corresponding initial point cloud cluster, retaining point clouds with a distance less than a preset plane distance threshold and aggregating them into a single point cloud dataset, where the preset plane distance threshold is 0.03m.

[0031] Specifically, the calculation of the directed bounding box (OBB) is as follows: First, perform PCA (Principal Component Analysis). PCA yields three principal directions: {PC1 (direction with the largest variance), PC2 (direction with the second largest variance), and PC3 (direction with the smallest variance)}. The coordinate system formed by these three principal directions is called the PCA coordinate system. The point cloud is first transformed to the PCA coordinate system, and the maximum and minimum coordinate points (max_point and min_point) are obtained. Then, the original coordinate system to which max_point and min_point are restored is denoted as […]. and This completes the calculation of the OBBs of all sub-point clouds in the point cloud cluster.

[0032] Because the local reflective point cloud does not cover the wall surface sufficiently, local irregularities may cause the feature vector calculated for the entire wall surface using PCA to be skewed by local noise. Therefore, increasing the coverage of the wall surface point cloud can effectively reduce the impact of local noise. Meanwhile, according to the Davis-Kahan theorem, the estimation error of the normal vector... It is readily apparent that increasing the sampling size n can effectively reduce the error in normal vector estimation. Therefore, it is necessary to use the point cloud set of highly reflective markers. Expand outwards to read as many point cloud data points as possible related to the wall surface to improve the final estimate of the plane on which the wall surface is located.

[0033] Then, based on the initial search range, the search range is iteratively expanded step by step in the world coordinate system. Each iteration expands the search range to the world coordinate system... X , Y , Z Extend by 0.3m in each direction, and iterate at least 10 times. The specific iteration method is as follows: S421, Obtain a pair of diagonal vertices of the directed bounding box. and , , All are point cloud coordinates of vertices, with an extended region of r = 0.3 =(1,1,1); S422, let , Calculate the expanded region after the first iteration. If point clouds exist in the expanded region after the first iteration... satisfy Then dotted clouds The point cloud after the search, These are parallel condition symbols, meaning they satisfy... and and dot clouds The point cloud after the search. Point cloud set. Any point in the cloud After the above operations, expand outward by 0.3m to obtain the point cloud. Repeat the operation more than 10 times to obtain the desired result. X , Y , Z Point cloud extending 3m in each direction , in turn Perform the above operations on all point clouds to obtain... After the above 10 expansions, the point cloud of the wall surface within a 3m range can finally be obtained.

[0034] Finally, the distance from the target point cloud after the search to the plane fitted to the corresponding initial point cloud cluster is calculated. Maintain distance Point clouds with distances less than a preset planar distance threshold are grouped together into a single point cloud dataset. Distance The calculation formula is: ,in These are the coordinates of the point cloud after the search. a ,b , c These are the fitting planes corresponding to the initial point cloud clusters. of a i , b i , c i .

[0035] In some embodiments, principal component analysis is used to analyze the point cloud data of each wall surface after expansion in S5. A plane fitting is performed on all point clouds in the dataset, and the result is denoted as... .

[0036] In some embodiments, S6 includes: S61, manually specifying or automatically matching a pair of plane equations corresponding to the parallel wall surfaces measured in S1 in the point cloud map; S62, calculating the average distance for each pair of plane equations and using it as the measured distance; S63, calculating the root mean square error between the measured distance of each parallel wall surface and the actual distance recorded in S1 and using it as the absolute distance error.

[0037] Specifically, display All point clouds are used to manually specify or automatically match the wall surfaces that require subsequent operations through interactive operations, and recorded as follows: , ,Will The specified wall faces correspond one-to-one with the wall faces filtered by S1. Use The distance between the planes represented by each pair of point clouds is calculated. For example, the calculation results in S5 It contains All point cloud plane fitting information, let The corresponding plane fitting information is traversal For all points in the array, calculate the distance from each point to... Given the distances, select the point with the smallest distance and denote it as . Similarly from Selected points ,calculate arrive Distance D1 and arrive The distance D2 is the average of D1 and D2, denoted as . Calculate using this method The distance between each point cloud pair is recorded as Calculate the root mean square error between the measured distances to each parallel wall surface and the actual distances recorded in S1. .

[0038] In some embodiments, S7 includes: S71, dividing the fitted plane equation into sections perpendicular to... x axis, y shaft and z The three sets of axes, when the normal vector of the fitted plane equation is in x When the components on the coordinate axes are at their maximum, the plane equation is classified as perpendicular to the coordinate axes. x A set of axes, if in y When the component on the coordinate axis is at its maximum, classify it to be perpendicular to the axis. y A set of axes, if in z When the component on the coordinate axis is at its maximum, classify it to be perpendicular to the axis. z S72. Normalize the normal vectors of all plane equations in each group to obtain three normalized normal vectors; S73. Calculate the mean, length of the average vector, and error of the three normalized normal vectors in turn to obtain the direction consistency error.

[0039] Specifically, let the fitted equation of any plane be... The normal vector is (a', b', c'), and then it is classified. If it is largest in |a'| (x-component), it means the normal vector is mainly along the x-axis, so the plane is most perpendicular to the x-axis and therefore parallel to the YZ plane. If it is largest in |b'| (y-component), it means the normal vector is mainly along the y-axis, so the plane is most perpendicular to the y-axis and therefore parallel to the XZ plane. If it is largest in |c'| (z-component), it means the normal vector is mainly along the z-axis, so the plane is most perpendicular to the z-axis and therefore parallel to the XY plane. Following the above method, the normal vector is classified as follows: Classify all planes in the array, and... Divided into three groups, namely, recording , , Normalize the normal vectors of all plane equations in each group. This yields three normalized normal vectors. , , Then, the mean, length of the average vector, and error are calculated sequentially for the three normalized normal vectors: first, the mean is calculated. , Then, the average vector length is calculated. , , Finally, the consistency error is calculated to obtain the directional consistency error. , , .

[0040] Based on the above calculations, the accuracy of LiDAR scanning and mapping is quantitatively evaluated using absolute distance error and directional consistency error. A smaller absolute distance error indicates higher accuracy, and vice versa; similarly, a consistency error closer to 0 indicates higher accuracy, and vice versa.

[0041] Finally, it should be noted that the above embodiments are merely preferred embodiments of the present invention used to illustrate the technical solutions of the present invention, and are not intended to limit the invention, nor are they intended to limit the patent scope of the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention. That is to say, any changes or refinements made to the main design concept and spirit of the present invention that are not of substantial significance, but whose technical problems are still consistent with the present invention, should be included within the protection scope of the present invention. In addition, the direct or indirect application of the technical solutions of the present invention to other related technical fields are similarly included within the patent protection scope of the present invention.

Claims

1. A method for evaluating the accuracy of indoor lidar scanning mapping, characterized in that, Includes the following steps: S1. Select the scene to be evaluated with parallel walls, paste reflective markers on the walls, and use a high-precision ranging device to measure and record the actual distance between each parallel wall. S2. Use a lidar scanning device to scan the scene to be evaluated and generate a point cloud map; S3. Based on the point cloud intensity value, identify and extract the point cloud clusters corresponding to the reflection markers from the point cloud map; S4. Using each point cloud cluster as the core, gradually expand outward to extract the complete point cloud data of the wall surface. S5. Perform plane fitting on the point cloud data of each expanded wall surface to obtain the plane equation corresponding to each wall surface. S6. Based on the plane equation, calculate the measured distance between parallel wall surfaces in the point cloud map, compare the measured distance with the actual distance recorded in S1, and calculate the absolute distance error. S7. Analyze the normal vector of the fitted plane equation and calculate the direction consistency error; S8. The accuracy of lidar scanning mapping is quantitatively evaluated based on absolute distance error and direction consistency error.

2. The method for evaluating the accuracy of indoor lidar scanning mapping according to claim 1, characterized in that, S1 includes: S11. In an indoor environment where the mapping accuracy needs to be evaluated, select multiple sets of candidate parallel wall faces. Use a high-precision distance measuring device to measure the distance between the same wall faces at multiple different locations and calculate the variance. Select wall faces with a variance less than a first preset threshold for evaluation. The first preset threshold is 3mm. 2 S12. Affix reflective markers to each of the selected wall surfaces, and accurately measure and record the actual distance between each parallel wall surface.

3. The method for evaluating the accuracy of indoor lidar scanning mapping according to claim 1, characterized in that, The reflective markers are reflective strips, and the reflective strips are at least 30cm in size. 30cm.

4. The method for evaluating the accuracy of indoor lidar scanning mapping according to claim 1, characterized in that, Step S3 includes: S31, filtering based on the intensity attributes of the point cloud, extracting points in the point cloud map whose intensity values ​​are higher than a second preset threshold to form a set of high-brightness cloud points, where the second preset threshold is the maximum value of the radar intensity range of the lidar; S32, performing Euclidean clustering analysis on the set of high-brightness cloud points, dividing spatially adjacent high-brightness cloud points into different point cloud clusters, with each point cloud cluster corresponding to a reflective marker.

5. The method for evaluating the accuracy of indoor lidar scanning mapping according to claim 1, characterized in that, S4 includes: S41. Calculate the directed bounding box of each point cloud cluster and use it as the initial search range; S42. Based on the initial search range, iteratively expand the region in the world coordinate system; after each iteration of expansion, search for point clouds within the expanded region; S43. Calculate the distance from the searched point cloud to the plane fitted to the corresponding initial point cloud cluster, retain the point clouds whose distance is less than the preset plane distance threshold, and aggregate them into a single point cloud dataset.

6. The method for evaluating the accuracy of indoor lidar scanning mapping according to claim 5, characterized in that, In S42, each iteration is directed to the world coordinate system. X , Y , Z Expand by 0.3m in each direction, and iterate at least 10 times; The iterative method is as follows: S421, Obtain a pair of diagonal vertices of the directed bounding box. and , , All are point cloud coordinates of vertices, with an extended region of r = 0.3 =(1,1,1); S422, let , Calculate the expanded region after the first iteration. If point clouds exist in the expanded region after the first iteration... satisfy Then dotted clouds The point cloud after the search, For parallel condition symbols; S423, each point cloud cluster must repeat operations S421 and S422 at least 10 times; In S43, the preset plane distance threshold is 0.03m; In S43, distance The calculation formula is: ,in These are the coordinates of the point cloud after the search. a , b , c These are the fitting planes corresponding to the initial point cloud clusters. of a i , b i , c i .

7. The method for evaluating the accuracy of indoor lidar scanning mapping according to claim 1, characterized in that, In S5, principal component analysis is used to perform planar fitting on the point cloud data of each expanded wall surface.

8. The method for evaluating the accuracy of indoor lidar scanning mapping according to claim 1, characterized in that, S6 include: S61. In the point cloud map, manually specify or automatically match a pair of plane equations corresponding to the parallel wall surfaces measured in S1; S62. Calculate the average distance for each pair of plane equations and use it as the measured distance; S63. Calculate the root mean square error between the measured distance and the actual distance recorded in S1 for each parallel wall surface and use it as the absolute distance error.

9. The method for evaluating the accuracy of indoor lidar scanning mapping according to claim 1, characterized in that, S7 includes: S71, dividing the fitted plane equation into those perpendicular to... x axis, y shaft and z The three sets of axes, when the normal vector of the fitted plane equation is in x When the components on the coordinate axes are at their maximum, the plane equation is classified as perpendicular to the coordinate axes. x A set of axes, if in y When the component on the coordinate axis is at its maximum, classify it to be perpendicular to the axis. y A set of axes, if in z When the component on the coordinate axis is at its maximum, classify it to be perpendicular to the axis. z S72. Normalize the normal vectors of all plane equations in each group to obtain three normalized normal vectors; S73. Calculate the mean, length of the average vector, and error of the three normalized normal vectors in turn to obtain the direction consistency error.

10. The method for evaluating the accuracy of indoor lidar scanning mapping according to claim 1, characterized in that, In S8, the smaller the absolute distance error, the higher the accuracy of the LiDAR scanning and mapping, and vice versa; the closer the consistency error is to 0, the higher the accuracy of the LiDAR scanning and mapping, and vice versa.

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