A cross-platform laser point cloud data registration quality evaluation method

Abstract

The present invention belongs to the technical field of point cloud data processing, and specifically relates to a cross-platform laser point cloud data registration quality evaluation method, which includes the following steps: extracting spatial geometric features of points, lines, and planes from the laser point clouds acquired on each platform after cross-platform laser point cloud registration; constructing a cross-platform laser point registration quality evaluation index based on spatial point features, a cross-platform laser point registration quality evaluation index based on spatial line features, and a cross-platform laser point registration quality evaluation index based on spatial plane features, and combining them to form a cross-platform laser point cloud registration quality evaluation index based on spatial features; and performing a comprehensive metric evaluation of the cross-platform laser point cloud registration quality after cross-platform laser point cloud registration using the cross-platform laser point cloud registration quality evaluation index based on spatial features. The present invention can more comprehensively and accurately evaluate the quality of multi-source point cloud data registration.

Description

Technical Field

[0001] The present invention belongs to the technical field of point cloud data processing, and in particular relates to a cross-platform laser point cloud data registration quality evaluation method. Background Art

[0002] With the rapid development of laser scanning technology, laser point cloud data has become an important data source for numerous fields, including geospatial information acquisition, urban planning, environmental monitoring, and autonomous driving. However, in practical applications, due to the complex and ever-changing data acquisition environment and performance differences between different platforms and sensors, the acquired laser point cloud data suffers from diversity, multi-source, and heterogeneity. Therefore, accurate registration of laser point cloud data across platforms has become crucial for data fusion applications, as the quality of registration directly impacts the accuracy and reliability of subsequent data processing and applications.

[0003] Numerous studies have been conducted on multi-platform laser point cloud data registration methods, encompassing a wide range of approaches. However, research on registration quality assessment is limited and lacks systematicity. Existing laser point cloud data registration quality assessment methods are mostly based on a single feature or metric, making it difficult to fully reflect the variability and uncertainty in scale, rotation, and translation of multi-source, cross-platform point cloud data. Summary of the Invention

[0004] In view of the above deficiencies in the prior art, the present invention provides a cross-platform laser point cloud data registration quality evaluation method, which can more comprehensively and accurately evaluate the quality of multi-source point cloud data registration.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is a cross-platform laser point cloud data registration quality evaluation method, comprising the following steps:

[0006] S1. Extract the spatial geometric features of points, lines, and planes from the laser point clouds acquired on each platform after cross-platform registration (referring to the alignment and fusion of lidar point cloud data from different sources or platforms through technical means to accurately reflect real-world spatial information in the same coordinate system; these platforms include drones, terrestrial laser scanners, and vehicle-mounted lidar systems. The registration process can overcome the shortcomings of a single data source, improve data integrity and accuracy, and enhance the detail and continuity of three-dimensional spatial information; this process can be achieved using publicly known technologies).

[0007] S2. Construct a cross-platform laser point registration quality evaluation index based on spatial point features;

[0008] S3. Construct a cross-platform laser point registration quality evaluation index based on spatial line features;

[0009] S4. Construct a cross-platform laser point registration quality evaluation index based on spatial plane features;

[0010] S5, combining the indicators in S2-S4 to form a cross-platform laser point cloud registration quality evaluation indicator based on spatial features;

[0011] S6. For the registered cross-platform laser point cloud, a comprehensive measurement evaluation of the cross-platform laser point cloud registration quality is performed using a cross-platform laser point cloud registration quality evaluation index based on spatial features.

[0012] As a preferred solution of the present invention, in said S2, the process of constructing a cross-platform laser point registration quality evaluation index based on spatial point features is:

[0013] S21. For the cross-platform laser point cloud after registration of two platforms, k pairs of feature points with the same name are extracted from the laser point clouds of the two platforms, which are expressed as 、 , i=1, 2, ..., k; where and Represents the coordinates of the corresponding point in the Cartesian coordinate system;

[0014] S22. Cross-platform laser point registration quality evaluation indicators based on spatial point features include: and ,in It is an indicator for evaluating the average deviation between cross-platform laser point clouds after registration. It is an indicator to evaluate the average deviation direction between cross-platform laser point clouds after registration, which can be expressed as:

[0015] (1);

[0016] (2);

[0017] Where α and β are weight coefficients; Representation matrix The corresponding maximum eigenvalue; Representation matrix The eigenvector corresponding to the largest eigenvalue; Represents a normalized vector; the superscript T represents the transpose.

[0018] As a preferred solution of the present invention, in S22, the values of α and β are both 1 / 2.

[0019] As a preferred solution of the present invention, in S3, the process of constructing a cross-platform laser point registration quality evaluation index based on spatial straight line features is as follows:

[0020] S31. Extract c pairs of characteristic straight lines with the same name from the laser point clouds of the two platforms, respectively. and , j = 1, 2, ..., c; 、 The corresponding direction vectors are 、 ;

[0021] S32. The quality evaluation indicators of cross-platform laser point registration based on spatial straight line features include and ,in It is an indicator to evaluate the degree of rotation between cross-platform laser point clouds after registration. It is an indicator for evaluating the rotation direction between cross-platform laser point clouds after registration, which can be expressed as:

[0022] (3);

[0023] (4);

[0024] Where, Representation matrix The eigenvector corresponding to the largest eigenvalue.

[0025] As a preferred solution of the present invention, in said S4, the process of constructing a cross-platform laser point registration quality evaluation index based on spatial plane features is:

[0026] S41. Extract h pairs of identical facets with complete boundaries from the laser point clouds of the two platforms, and calculate the areas and normal vectors of the identical facets (using known techniques), assuming their areas are 、 , the normal vectors are 、 ,f=1,2,……,h;

[0027] S42. The quality evaluation indicators of cross-platform laser point registration based on spatial plane features include and ,in It is an indicator for evaluating the scale consistency between cross-platform laser point clouds after registration. It is an indicator to evaluate the consistency of the rotation direction between the laser point clouds across platforms after registration, which can be expressed as:

[0028] (5);

[0029] (6);

[0030] Where, is a matrix The eigenvector corresponding to the largest eigenvalue.

[0031] As a preferred embodiment of the present invention, in said S5, 、 、 、 、 、 Together they constitute a cross-platform laser point cloud registration quality evaluation index based on spatial features.

[0032] As a preferred solution of the present invention, in S6, the process of performing comprehensive metric evaluation of cross-platform laser point cloud registration quality is as follows:

[0033] S61. Divide the registered cross-platform laser point cloud into M regions according to the density of the laser point cloud.

[0034] S62, extracting spatial geometric features of points, lines, and planes in each region. If no line features or surface element features can be extracted from a region, the number of lines or surfaces (i.e., plane features) with the same name in the region is zero.

[0035] S63. According to the spatial geometric features extracted in each area, the cross-platform laser point cloud registration quality evaluation index based on spatial features of each area is calculated, including 、 、 、 、 、 ,m=1,2,……,M, 、 、 、 、 、 That is the mth region 、 、 、 、 、 ;

[0036] S64. Calculate the comprehensive evaluation index of each area separately 、 ,in It is an indicator to evaluate the degree of alignment between laser point clouds in the cross-platform area after registration. It is an indicator for evaluating the alignment direction between laser point clouds in the cross-platform area after registration, and is expressed as:

[0037] (7);

[0038] (8);

[0039] Where, 、 、 are the weights of the registration quality evaluation indexes of point, line and plane features in the mth region respectively;

[0040] S65, according to the calculated areas and , generate heat maps and vector maps (using known technologies), and visually display the distribution of laser point cloud registration quality in each area;

[0041] S66, calculate M areas and The average value of the registration quality comprehensive metric of the laser point cloud in the entire area is obtained 、 ,in It is an indicator for evaluating the comprehensive alignment between cross-platform laser point clouds after registration. It is an indicator for evaluating the comprehensive alignment direction between cross-platform laser point clouds after registration, which can be expressed as:

[0042] (9);

[0043] (10).

[0044] As a preferred embodiment of the present invention, in the S61, the area division method is to establish a plane grid according to a preset size, then project the original laser point cloud into the corresponding grid, extract density features from the point set in each grid, and perform area division based on the density features.

[0045] As a preferred embodiment of the present invention, in the S62, the spatial geometric features of points, lines and planes are extracted in each area respectively, and then the corresponding features are matched to obtain the feature points, feature lines and surface elements with the same name in the area. Specifically, for points, the feature points with the same name are obtained through key point extraction, feature descriptor generation and descriptor matching; for line features, a depth map is generated according to the laser point cloud, and the line features are extracted and matched in the depth map; for plane features, plane fitting and clustering methods are used to extract planes, and plane feature matching is performed based on the criteria of minimum plane normal vector angle and maximum plane overlap area.

[0046] As a preferred embodiment of the present invention, in the S64, 、 、 The calculation method is:

[0047] (11);

[0048] (12);

[0049] (13);

[0050] Where, 、 、 are the numbers of feature points, feature lines and surface elements with the same name extracted in the mth region.

[0051] The algorithm involved in the present invention can be executed by an electronic device. The electronic device includes a memory, a processor, and a computer program stored in the memory and run on the processor. The algorithm is implemented by executing the program by the processor.

[0052] The beneficial effects of the present invention are:

[0053] The present invention comprehensively considers multiple spatial features such as points, lines, and planes, and constructs a multi-feature joint registration quality evaluation system, which effectively solves the problem of single quality evaluation indicators and single features in existing laser point cloud data registration. Compared with existing laser point cloud data registration quality evaluation methods, it can more comprehensively reflect the differences and uncertainties of multi-source point cloud data in terms of scale, rotation, translation, etc., more comprehensively and accurately evaluate the quality of multi-source point cloud data registration, and can be visualized to intuitively display the distribution of point cloud registration quality in each area. The application of the present invention can provide strong support for the precise registration and high-quality fusion of cross-platform laser point cloud data. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is a flow diagram of the present invention. DETAILED DESCRIPTION

[0055] The embodiments of the present invention are further described below with reference to the accompanying drawings:

[0056] like Figure 1 As shown, a cross-platform laser point cloud data registration quality evaluation method includes the following steps:

[0057] S1. Extract the spatial geometric features of points, lines, and planes from the laser point clouds obtained on each platform after cross-platform registration.

[0058] S2. Construct a cross-platform laser point registration quality evaluation index based on spatial point features;

[0059] S3. Construct a cross-platform laser point registration quality evaluation index based on spatial line features;

[0060] S4. Construct a cross-platform laser point registration quality evaluation index based on spatial plane features;

[0061] S5, combining the indicators in S2-S4 to form a cross-platform laser point cloud registration quality evaluation indicator based on spatial features;

[0062] S6. For the registered cross-platform laser point cloud, a comprehensive measurement evaluation of the cross-platform laser point cloud registration quality is performed using a cross-platform laser point cloud registration quality evaluation index based on spatial features.

[0063] In S2, the process of constructing a cross-platform laser point registration quality evaluation index based on spatial point features is as follows:

[0064] S21. For the cross-platform laser point cloud after registration of two platforms, point features are the most basic elements in laser point cloud data. They directly reflect the spatial position and shape of the object. K pairs of feature points with the same name are extracted from the laser point clouds of the two platforms, which are expressed as 、 , i=1, 2, ..., k; where and Represents the coordinates of the corresponding point in the Cartesian coordinate system;

[0065] S22. Cross-platform laser point registration quality evaluation indicators based on spatial point features include: and ,in It is an indicator for evaluating the average deviation between cross-platform laser point clouds after registration. It is an indicator to evaluate the average deviation direction between cross-platform laser point clouds after registration, which can be expressed as:

[0066] (1);

[0067] (2);

[0068] In the formula, α and β are weight coefficients, both of which are 1 / 2; Representation matrix The corresponding maximum eigenvalue; Representation matrix The eigenvector corresponding to the largest eigenvalue; Represents a normalized vector; the superscript T represents the transpose.

[0069] If it is for multiple platforms, that is, the number of platforms is greater than two, you can select any two platforms to calculate according to the method of this embodiment, and calculate the obtained and Find the average value and use the average value as the new and , used for subsequent indicator calculations, the same below.

[0070] In S3, the process of constructing a cross-platform laser point registration quality evaluation index based on spatial line features is as follows:

[0071] S31. Compared with point features, spatial line features can better reflect the contour and shape information of objects, thus having a unique advantage in evaluating the rotation error after registration. C pairs of feature lines with the same name are extracted from the laser point clouds of the two platforms, which are and , j = 1, 2, ..., c; 、 The corresponding direction vectors are 、 ;

[0072] S32. The quality evaluation indicators of cross-platform laser point registration based on spatial straight line features include and ,in It is an indicator to evaluate the degree of rotation between cross-platform laser point clouds after registration. It is an indicator for evaluating the rotation direction between cross-platform laser point clouds after registration, which can be expressed as:

[0073] (3);

[0074] (4);

[0075] Where, Representation matrix The eigenvector corresponding to the largest eigenvalue.

[0076] In S4, the process of constructing the cross-platform laser point registration quality evaluation index based on spatial plane features is as follows:

[0077] S41. Extract h pairs of surface elements with complete boundaries from the laser point clouds of the two platforms, and calculate the area and normal vector of the surface elements with the same name respectively. Let their areas be 、 , the normal vectors are 、 ,f=1,2,……,h;

[0078] S42. The quality evaluation indicators of cross-platform laser point registration based on spatial plane features include and ,in It is an indicator for evaluating the scale consistency between cross-platform laser point clouds after registration. It is an indicator to evaluate the consistency of the rotation direction between the laser point clouds across platforms after registration, which can be expressed as:

[0079] (5);

[0080] (6);

[0081] Where, is a matrix The eigenvector corresponding to the largest eigenvalue.

[0082] In S5, 、 、 、 、 、 Together they constitute a cross-platform laser point cloud registration quality evaluation index based on spatial features.

[0083] In S6, the process of comprehensive evaluation of cross-platform laser point cloud registration quality is as follows:

[0084] S61. Divide the registered cross-platform laser point cloud into M regions according to the density of the laser point cloud.

[0085] S62, extracting spatial geometric features of points, lines, and planes in each region. If no line features or surface element features can be extracted from a region, the number of feature lines or surface elements with the same name in the region is zero.

[0086] S63. According to the spatial geometric features extracted in each area, the cross-platform laser point cloud registration quality evaluation index based on spatial features of each area is calculated, including 、 、 、 、 、 ,m=1,2,……,M, 、 、 、 、 、 That is the mth region 、 、 、 、 、 ; Used to describe the differences and uncertainties in deviation, rotation, scale, etc. of the laser point cloud in the area after registration;

[0087] S64. Calculate the comprehensive evaluation index of each area separately 、 ,in It is an indicator to evaluate the degree of alignment between laser point clouds in the cross-platform area after registration. It is an indicator for evaluating the alignment direction between laser point clouds in the cross-platform region after registration. It is used to describe the overall difference and uncertainty of the laser point clouds in the region after registration, and is expressed as:

[0088] (7);

[0089] (8);

[0090] Where, 、 、 are the weights of the registration quality evaluation indexes of point, line and plane features in the mth region respectively;

[0091] S65, according to the calculated areas and , generate heat maps and vector maps to visually display the distribution of laser point cloud registration quality in each area;

[0092] S66, calculate M areas and The average value of the registration quality comprehensive metric of the laser point cloud in the entire area is obtained 、 ,in It is an indicator for evaluating the comprehensive alignment between cross-platform laser point clouds after registration. It is an indicator for evaluating the comprehensive alignment direction between cross-platform laser point clouds after registration, which can be expressed as:

[0093] (9);

[0094] (10).

[0095] In the S65 heat map, the color depth can be used to directly reflect the distribution of the registration quality index. The darker the color, the lower the registration quality of the area, while the lighter the color, the higher the registration quality. The vector diagram shows the distribution of the rotation direction index between the cross-platform point clouds.

[0096] In S61, the region division method uses a plane grid method to create a plane grid according to a pre-set size. The original laser point cloud is then projected onto the corresponding grid. The density features of the point set in each grid are extracted and the region division is performed based on the density features. This region division method can reflect the difference in local region registration quality.

[0097] In S62, the spatial geometric features of points, lines, and planes are extracted in each area respectively, and then the corresponding features are matched to obtain the feature points, feature lines, and surface elements with the same name in the area. Specifically, for points, the feature points with the same name are obtained through key point extraction, feature descriptor generation, and descriptor matching; for line features, a depth map is generated based on the laser point cloud, and line features are extracted and matched in the depth map; for plane features, plane fitting and clustering methods are used to extract planes, and plane feature matching is performed based on the criteria of minimum plane normal vector angle and maximum plane overlap area.

[0098] In S64, 、 、 The calculation method is:

[0099] (11);

[0100] (12);

[0101] (13);

[0102] Where, 、 、 are the numbers of feature points, feature lines and surface elements with the same name extracted in the mth region.

Claims

1. A cross-platform laser point cloud data registration quality evaluation method, characterized by The following steps are involved: S1. Extract the spatial geometric features of points, lines, and planes from the laser point clouds obtained on each platform after cross-platform registration. S2. Construct a cross-platform laser point registration quality evaluation index based on spatial point features; S3. Construct a cross-platform laser point registration quality evaluation index based on spatial line features; S4. Construct a cross-platform laser point registration quality evaluation index based on spatial plane features; S5, combining the indicators in S2-S4 to form a cross-platform laser point cloud registration quality evaluation indicator based on spatial features; S6. For the registered cross-platform laser point cloud, a comprehensive metric evaluation of the cross-platform laser point cloud registration quality is performed using a cross-platform laser point cloud registration quality evaluation index based on spatial features; In S2, the process of constructing a cross-platform laser point registration quality evaluation index based on spatial point features is as follows: S21. For the cross-platform laser point cloud after registration of two platforms, k pairs of feature points with the same name are extracted from the laser point clouds of the two platforms, which are expressed as 、 , i=1, 2, ..., k; where and Represents the coordinates of the corresponding point in the Cartesian coordinate system; S22. Cross-platform laser point registration quality evaluation indicators based on spatial point features include: and ,in It is an indicator for evaluating the average deviation between cross-platform laser point clouds after registration. It is an indicator to evaluate the average deviation direction between cross-platform laser point clouds after registration, which can be expressed as: （1）； （2）； Where α and β are weight coefficients; Representation matrix The corresponding maximum eigenvalue; Representation matrix The eigenvector corresponding to the largest eigenvalue; represents a normalized vector; the superscript T represents transpose; In the aforementioned S3, the process of constructing a cross-platform laser point registration quality evaluation index based on spatial straight line features is as follows: S31. Extract c pairs of characteristic straight lines with the same name from the laser point clouds of the two platforms, respectively. and , j = 1, 2, ..., c; 、 The corresponding direction vectors are 、 ; S32. The quality evaluation indicators of cross-platform laser point registration based on spatial straight line features include and ,in It is an indicator to evaluate the degree of rotation between cross-platform laser point clouds after registration. It is an indicator for evaluating the rotation direction between cross-platform laser point clouds after registration, which can be expressed as: （3）； （4）； Where, Representation matrix The eigenvector corresponding to the largest eigenvalue; In the aforementioned S4, the process of constructing a cross-platform laser point registration quality evaluation index based on spatial plane features is as follows: S41. Extract h pairs of surface elements with complete boundaries from the laser point clouds of the two platforms, and calculate the area and normal vector of the surface elements with the same name respectively. Let their areas be 、 , the normal vectors are 、 ,f=1,2,……,h; S42. The quality evaluation indicators of cross-platform laser point registration based on spatial plane features include and ,in It is an indicator for evaluating the scale consistency between cross-platform laser point clouds after registration. It is an indicator to evaluate the consistency of the rotation direction between the laser point clouds across platforms after registration, which can be expressed as: （5）； （6）； Where, is a matrix The eigenvector corresponding to the largest eigenvalue.

2. A cross-platform laser point cloud data registration quality evaluation method according to claim 1, characterized in that: In the above S22, the values of α and β are both 1 / 2.

3. The cross-platform laser point cloud data registration quality evaluation method according to claim 1, characterized in that: In the S5, 、 、 、 、 、 Together they constitute a cross-platform laser point cloud registration quality evaluation index based on spatial features.

4. A cross-platform laser point cloud data registration quality evaluation method according to claim 3, characterized in that: In S6, the process of performing comprehensive metric evaluation of cross-platform laser point cloud registration quality is as follows: S61. Divide the registered cross-platform laser point cloud into M regions according to the density of the laser point cloud. S62, extracting spatial geometric features of points, lines, and planes in each region. If no line features or surface element features can be extracted from a region, the number of feature lines or surface elements with the same name in the region is zero. S63. According to the spatial geometric features extracted in each area, the cross-platform laser point cloud registration quality evaluation index based on spatial features of each area is calculated, including 、 、 、 、 、 ,m=1,2,……,M, 、 、 、 、 、 That is the mth region 、 、 、 、 、 ; S64. Calculate the comprehensive evaluation index of each area separately 、 ,in It is an indicator to evaluate the degree of alignment between laser point clouds in the cross-platform area after registration. It is an indicator for evaluating the alignment direction between laser point clouds in the cross-platform area after registration, and is expressed as: （7）； （8）； Where, 、 、 are the weights of the registration quality evaluation indexes of point, line and plane features in the mth region respectively; S65, according to the calculated areas and , generate heat maps and vector maps to visually display the distribution of laser point cloud registration quality in each area; S66, calculate M areas and The average value of the registration quality comprehensive metric of the laser point cloud in the entire area is obtained 、 ,in It is an indicator for evaluating the comprehensive alignment between cross-platform laser point clouds after registration. It is an indicator for evaluating the comprehensive alignment direction between cross-platform laser point clouds after registration, which can be expressed as: （9）； （10）。 5. A cross-platform laser point cloud data registration quality evaluation method according to claim 4, characterized in that: In the above-mentioned S61, the region division method is to establish a plane grid according to a preset size, then project the original laser point cloud into the corresponding grid, extract density features from the point set in each grid, and perform region division based on the density features.

6. A cross-platform laser point cloud data registration quality evaluation method according to claim 4, characterized in that: In the above-mentioned S62, the spatial geometric features of points, lines and planes are extracted in each area respectively, and then the corresponding features are matched to obtain the feature points, feature lines and surface elements with the same name in the area. Specifically, for points, the feature points with the same name are obtained through key point extraction, feature descriptor generation and descriptor matching; for line features, a depth map is generated according to the laser point cloud, and the line features are extracted and matched in the depth map; for plane features, plane fitting and clustering methods are used to extract planes, and plane feature matching is performed based on the criteria of minimum plane normal vector angle and maximum plane overlap area.

7. The cross-platform laser point cloud data registration quality evaluation method according to claim 4, characterized in that: In the S64, 、 、 The calculation method is: （11）； （12）； （13）； Where, 、 、 are the numbers of feature points, feature lines and surface elements with the same name extracted in the mth region.

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