Error calibration method and system for 3D laser scanners

Abstract

This invention relates to the field of image correction technology, specifically to an error calibration method and system for a 3D laser scanner. The method includes: obtaining 3D point cloud data in various scanning directions using a 3D laser scanner; pooling any number of 3D point cloud data to obtain several boundary feature curves in the pooled point cloud data; filtering the pooled point cloud data to obtain target point cloud data, and further filtering to identify reference point clouds; adjusting the positions of feature points in the reference point clouds and merging adjacent feature points to obtain regions with consistent adjustment trends; using the overall adjustment of feature points in the regions with consistent adjustment trends to move and adjust data points other than feature points in the reference point clouds; after adjusting the positions of all data points in all reference point clouds, obtaining new reference point clouds; and registering and stitching all new reference point clouds together. This invention improves the accuracy of 3D point cloud data acquired by a 3D laser scanner.

Description

Technical Field

[0001] This invention relates to the field of image correction technology, and more specifically to an error calibration method and system for three-dimensional laser scanners. Background Technology

[0002] Point cloud error calibration is crucial in the registration process because it directly affects registration accuracy and result reliability. By calibrating systematic errors (such as inherent scanner bias) and random errors (such as environmental noise), the convergence of registration algorithms (such as ICP) can be optimized, avoiding misalignment due to error accumulation. Especially in multi-station scanning or large-scale scenes, an accurate error model can improve the consistency of point cloud stitching, ensuring the accuracy of subsequent modeling and analysis.

[0003] Traditional methods match and stitch point clouds by comparing the matching degree of point cloud feature curves from multiple angles, without considering the distortion problem of the point cloud itself during scanning, resulting in inaccurate acquisition of the final point cloud of the object. Summary of the Invention

[0004] This invention provides an error calibration method and system for 3D laser scanners to solve existing problems.

[0005] The error calibration method and system for a 3D laser scanner of the present invention adopts the following technical solution:

[0006] One embodiment of the present invention provides an error calibration method for a 3D laser scanner, the method comprising the following steps:

[0007] The object is scanned by a 3D laser scanner with several scanning directions to obtain 3D point cloud data in each scanning direction;

[0008] Several 3D pooling windows of different sizes are constructed to pool any number of 3D point clouds to obtain pooled point cloud data and several boundary feature curves composed of several feature points in the pooled point cloud data. The consistency of the changing trends between the boundary feature curves is analyzed to obtain the matching relationship between the boundary feature curves. Based on the matching relationship between the boundary feature curves in the 3D point cloud data of all scanning directions, the pooled point cloud data is filtered to obtain the target point cloud data.

[0009] By utilizing the overall distribution level of the trend consistency corresponding to the boundary feature curves with matching relationships in different target point cloud data, reference curves are selected, and reference point clouds are selected by the number and length of reference curves in the target point cloud data; the position of feature points in the reference point cloud is adjusted based on the matching relationship between the boundary feature curves.

[0010] By merging adjacent feature points based on the changes before and after feature point position adjustment, several regions with consistent adjustment trends are obtained. The overall adjustment of feature points in the regions with consistent adjustment trends is used to move and adjust data points other than feature points in the reference point cloud. After adjusting the positions of all data points in all reference point clouds, a new reference point cloud is obtained.

[0011] Register and stitch together all new reference point clouds.

[0012] Furthermore, the specific method for constructing several three-dimensional pooling windows of different sizes to pool any three-dimensional point cloud data to obtain pooled point cloud data and several boundary feature curves composed of several feature points in the pooled point cloud data includes:

[0013] The preset side length is A 3D pooling window is used to perform a sliding traversal on the 3D point cloud data. During the sliding traversal, max pooling is used to downsample the 3D point cloud data. Max pooling of any 3D pooling window with an arbitrary side length is used as a pooling method to obtain pooled point cloud data under any pooling method and several feature points contained in the pooled point cloud data. The side length of the 3D pooling window is given. Furthermore, the size of the 3D pooling window is within the range Inner The step size is increased, and the sliding compensation of the 3D pooling window during the sliding traversal is... ,in , , as well as These are the preset first side length parameters, second side length parameters, first step length parameters, and second step length parameters, respectively.

[0014] Under any pooling method, the distance between feature points is obtained, and feature points with a distance less than a preset distance threshold are connected to each other to obtain several boundary feature curves under the corresponding pooling method.

[0015] Furthermore, the specific method for obtaining the consistency of the changing trends among the boundary feature curves is as follows:

[0016] For any two 3D point cloud data with different scanning directions, the DDTW algorithm is used to obtain the shortest alignment path between any two boundary feature curves in the two 3D point cloud data and the correspondence between feature points in the two boundary feature curves. For any one of the two boundary feature curves, it is denoted as the first target boundary feature curve, and the other boundary feature curve is denoted as the second target boundary feature curve. The number of feature points on the first target boundary feature curve that have a correspondence with the second target boundary feature curve is counted and denoted as the matching parameter. The number of feature points on the two boundary feature curves whose matching parameter is greater than a preset first parameter is denoted as the overmatch parameter between the two boundary feature curves. Based on the length of the shortest alignment path and the overmatch parameter between the two boundary feature curves, the consistency of the change trend between the two boundary feature curves is obtained, wherein the length of the shortest alignment path and the overmatch parameter are both negatively correlated with the consistency of the change trend.

[0017] Furthermore, the specific method for filtering the pooled point cloud data to obtain the target point cloud data based on the matching relationship between boundary feature curves in the 3D point cloud data across all scanning directions includes:

[0018] When the consistency of the change trend is greater than the preset consistency threshold, the corresponding second target boundary feature curve is used as the matching curve of the corresponding first target boundary feature curve; under arbitrary pooling mode, the number of matching curves of arbitrary boundary feature curves in 3D point cloud data in arbitrary scanning direction is obtained and recorded as the matching quantity of the boundary feature curve; and the total number of feature points in all 3D point cloud data under arbitrary pooling mode is counted.

[0019] The pooling suitability is calculated based on the side length of the pooling window under any pooling method, the matching amount of all boundary feature curves in the 3D point cloud data in all scanning directions, and the total number of feature points in the 3D point cloud data in all scanning directions. The side length of the pooling window and the matching amount of all boundary feature curves in the 3D point cloud data in all scanning directions are positively correlated with the pooling suitability, while the total number of feature points in the 3D point cloud data in all scanning directions is negatively correlated with the pooling suitability.

[0020] Obtain the pooling method that maximizes the pooling suitability among all pooling methods, and denote it as the target pooling method. Then, denote the pooled point cloud data obtained under the target pooling method as the target point cloud data.

[0021] Furthermore, the method for selecting reference curves by utilizing the overall distribution level of trend consistency corresponding to boundary feature curves with matching relationships in different target point cloud data includes:

[0022] For target point cloud data in any scanning direction, the number of target point cloud data corresponding to any boundary feature curve in the target point cloud data in all other scanning directions is obtained, and this number is denoted as the matching inclusion of the boundary feature curve. The target point cloud data in any scanning direction is denoted as the first point cloud data, and any boundary feature region in the first point cloud data is denoted as the first boundary feature curve. The maximum trend consistency of all matching curves of the first boundary feature curve in any target point cloud data other than the first point cloud data is obtained, and this maximum trend consistency of the first boundary feature curve in any target point cloud data other than the first point cloud data is denoted as the maximum trend consistency of the first boundary feature curve in any target point cloud data other than the first point cloud data. The average value of the maximum trend consistency of the first boundary feature curve in any target point cloud data other than the first point cloud data is obtained, and this average value is denoted as the consistency parameter of the first boundary feature curve. Based on the matching inclusion and consistency parameter of the arbitrary boundary feature curve, the true conformity of the boundary feature curve is calculated, wherein the matching inclusion and the consistency parameter are both positively correlated with the true conformity.

[0023] When the actual compliance is greater than the preset second parameter, the corresponding boundary feature curve is recorded as the reference curve.

[0024] Furthermore, the specific method for filtering the reference point cloud by the number and length of reference curves in the target point cloud data includes:

[0025] Obtain the number of reference curves contained in the target point cloud data in any scanning direction, and the number of feature points contained in all reference curves in the target point cloud data; combine the number of reference curves in the target point cloud data in the scanning direction, and the proportion of feature points contained in all reference curves in the target point cloud data to the total number of feature points in all target point cloud data, to calculate the reference coefficient of the target point cloud data in the scanning direction, wherein the number of reference curves and the proportion of feature points are both positively correlated with the reference coefficient; the target point cloud data with the largest reference coefficient among all target point cloud data in the scanning directions is taken as the reference point cloud.

[0026] Furthermore, the specific method for adjusting the position of feature points in the reference point cloud based on the matching relationship between boundary feature curves includes:

[0027] Obtain the maximum value among the adjustment coefficients of the boundary feature curve and all corresponding matching curves. Take the matching point in the matching curve corresponding to the maximum adjustment coefficient as the reference point of the matching point in the boundary feature curve. According to the relative positional relationship between the reference points in the matching curves corresponding to the boundary feature curve, adjust the position of the feature points in the boundary feature curve so that the relative positional relationship between the feature points in the boundary feature curve is the same as the relative positional relationship between the reference points in the corresponding matching curves. Record the Euler angle and the distance before and after the position change of the feature points in the boundary feature curve, respectively, as the adjusted Euler angle and the adjusted distance.

[0028] Furthermore, the method for merging adjacent feature points based on the changes in their positions before and after adjustment to obtain several regions with consistent adjustment trends includes:

[0029] For the reference point cloud after adjusting the position of feature points in the boundary feature curve, the sum of the absolute values ​​of the difference in adjustment distance and the absolute values ​​of the difference in adjustment Euler angle between adjacent feature points in the reference point cloud is obtained and recorded as the first difference and the second difference, respectively. Based on the first difference and the second difference, the feasibility of merging adjacent feature points is obtained. The first difference and the second difference are both negatively correlated with the feasibility of merging. When the feasibility of merging is greater than or equal to the preset fourth parameter, the corresponding two feature points are merged. After merging all adjacent feature points with a feasibility of merging greater than or equal to the fourth parameter, several regions with consistent adjustment trends are obtained.

[0030] Furthermore, the specific method for moving and adjusting data points other than feature points in the reference point cloud by utilizing the overall adjustment of feature points in the region with consistent adjustment trends includes:

[0031] For any region with consistent adjustment trends, the feature point with the largest adjustment distance within the region is identified and designated as the main feature point. The adjustment distance of the main feature point is taken as the adjustment length of the region with consistent adjustment trends, and the adjustment Euler angle of the main feature point is taken as the adjustment scanning direction of the region with consistent adjustment trends. Data points other than feature points in the reference point cloud are designated as non-feature points. The region with consistent adjustment trends closest to any non-feature point is identified and designated as the target region of the non-feature point. For any non-feature point, the midpoint of the boundary feature curve closest to the non-feature point in the target region is identified, and the straight line passing through the midpoint and with the scanning direction of the target region is taken as the adjustment trend line of the target region. The angle between the line connecting the non-feature point and the midpoint and the adjustment trend line is identified and designated as the first adjustment parameter of the non-feature point. The minimum distance between the non-feature point and the adjustment trend line is identified and designated as the second adjustment parameter of the non-feature point. The adjustment influence of the non-feature point is obtained based on the first and second adjustment parameters. Combining the adjustment length of the target region of the non-feature point and the adjustment influence of the non-feature point, the movement distance of the non-feature point along the adjustment scanning direction of the corresponding target region is obtained.

[0032] An error calibration system for a 3D laser scanner includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the error calibration method for a 3D laser scanner.

[0033] The beneficial effects of the technical solution of this invention are as follows: By analyzing the changing trends between boundary feature curves, precise matching of feature points can be achieved, thereby optimizing the subsequent registration and stitching process of point cloud data. In addition, by utilizing the matching relationship of boundary feature curves, a reference curve consistent with the overall trend can be selected, thereby improving the selection accuracy of reference point cloud data and enabling effective adjustment of feature point positions. Furthermore, by utilizing the adjustment of feature point positions and the merging of regions with consistent trends, the matching accuracy of adjacent feature points can be effectively improved, making the final reference point cloud more accurate and thus improving the registration effect of point cloud data. Moreover, by adjusting the positions of data points in all reference point clouds, the consistency of all data points globally is ensured, resulting in a more realistic and accurate new reference point cloud, thereby improving the accuracy of 3D point cloud data acquired by a 3D laser scanner. Attached Figure Description

[0034] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0035] Figure 1 This is a flowchart illustrating the steps of the error calibration method for a 3D laser scanner according to the present invention. Detailed Implementation

[0036] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of the error calibration method and system for a 3D laser scanner proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0037] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0038] The following description, in conjunction with the accompanying drawings, details the specific scheme of the error calibration method and system for a 3D laser scanner provided by this invention.

[0039] Please see Figure 1 The diagram illustrates a flowchart of an error calibration method for a 3D laser scanner according to an embodiment of the present invention, the method comprising the following steps:

[0040] Step S001: Scan the object using a 3D laser scanner with several scanning directions to obtain 3D point cloud data for each scanning direction.

[0041] It is important to note that error calibration in point clouds is crucial in the registration process, as it directly affects registration accuracy and result reliability. By calibrating systematic errors (such as inherent scanner bias) and random errors (such as environmental noise), the convergence of registration algorithms (such as ICP) can be optimized, avoiding misalignment due to error accumulation. Especially in multi-station scanning or large scenes, an accurate error model can improve the consistency of point cloud stitching, ensuring the accuracy of subsequent modeling and analysis. Lack of error calibration may lead to registration failure or error propagation, affecting overall data quality. This invention determines the most data-efficient downsampling method by comparing the matching performance of point clouds at different angles under different downsampling conditions. A reference point cloud is selected based on the degree of matching between the boundary feature curves in a single-angle point cloud and other point clouds, and the positions of boundary feature points in the reference point cloud are adjusted. Based on the adjustment performance of feature points in the boundary feature curves, the positions of other points in the point cloud are adjusted, enabling accurate stitching of point clouds from multiple angles.

[0042] Specifically, in order to implement the error calibration method for a 3D laser scanner proposed in this embodiment, it is first necessary to acquire 3D point cloud data. The specific process is as follows:

[0043] First, several scanning points are set up, and a 3D laser scanner is installed at each scanning point. The object is scanned using the 3D laser scanner, ensuring that there is at least one [missing information] between adjacent scanning areas during the scanning process. The above overlaps yield the corresponding 3D point cloud data, which is then denoised using statistical filtering, where A is a preset scaling parameter.

[0044] It should be noted that the preset ratio parameter A is 30 based on experience, but it can be adjusted according to the actual situation. This embodiment of the invention does not impose any specific limitations.

[0045] Then, the preset side length is... A 3D pooling window is used to perform convolution processing on the 3D point cloud data, and local region product summation is performed. A non-linear activation function is then applied after convolution to enhance the model's expressive power and obtain the feature map of the convolutional layer.

[0046] It should be noted that the side length of the 3D pooling window is preset based on experience. The value is 3, which can be adjusted according to the actual situation; in addition, the nonlinear activation function described in the embodiments of the present invention may include at least ReLU and PReLU, which can be adjusted according to the actual situation, and the embodiments of the present invention do not make specific limitations.

[0047] Finally, max pooling is performed on the feature map through a multi-level pooling window. The pooling results from different levels are concatenated along the channel dimension to form a fixed-length feature vector. This fixed-length feature vector is then input into a fully connected layer to obtain feature points.

[0048] Thus, the three-dimensional point cloud data and several feature points in the three-dimensional point cloud data are obtained through the above method.

[0049] Step S002: Construct several 3D pooling windows of different sizes to pool any 3D point cloud data to obtain pooled point cloud data and several boundary feature curves composed of several feature points in the pooled point cloud data. Analyze the consistency of the changing trends between the boundary feature curves to obtain the matching relationship between the boundary feature curves. Based on the matching relationship between the boundary feature curves in the 3D point cloud data of all scanning directions, filter the pooled point cloud data to obtain the target point cloud data.

[0050] It should be noted that when directly performing point cloud registration using 3D point clouds of an object acquired from different original angles, the system requires a significant amount of computational power due to the large number of points contained in the 3D point cloud, necessitating the matching of numerous feature points from each angle. To shorten the point cloud registration time, a downsampling operation can be used to reduce the number of feature points in the 3D point cloud.

[0051] Specifically, in step S201, for three-dimensional point cloud data under any scanning direction, several three-dimensional pooling windows with different side lengths are constructed, and the three-dimensional point cloud data is pooled respectively to obtain pooled point cloud data under the corresponding three-dimensional pooling window and several boundary feature curves composed of several feature points in the pooled point cloud data. The consistency of the change trend between the boundary feature curves obtained by pooling windows with the same side length under any two scanning directions is analyzed.

[0052] First, the preset side length is... A 3D pooling window is used to perform a sliding traversal on the 3D point cloud data. During the sliding traversal, max pooling is used to downsample the 3D point cloud data. Max pooling of any 3D pooling window with an arbitrary side length is used as a pooling method to obtain pooled point cloud data under any pooling method and several feature points contained in the pooled point cloud data. The side length of the 3D pooling window is given. Furthermore, the size of the 3D pooling window is within the range Inner The step size is increased, and the sliding compensation of the 3D pooling window during the sliding traversal is... ,in , , as well as These are the preset first side length parameters, second side length parameters, first step length parameters, and second step length parameters, respectively.

[0053] It should be noted that, based on experience, the first side length parameter, the second side length parameter, the first step length parameter, and the second step length parameter are preset to be 3, 15, 1, and 1, respectively. They can be adjusted according to the actual situation, and the embodiments of the present invention do not impose specific limitations.

[0054] Then, under any pooling method, the distance between feature points is obtained, and feature points with a distance less than a preset distance threshold are connected to each other to obtain several boundary feature curves under the corresponding pooling method.

[0055] It should be noted that during the process of obtaining the boundary feature curve, there may be instances where the distance between multiple feature points in a local area is less than the distance threshold, resulting in a non-single-chain graph structure in the boundary feature curve. This makes it impossible to use the DDTW algorithm for subsequent analysis. Therefore, in this embodiment of the invention, for boundary feature curves with non-single-chain structures, feature points with more than 2 connected feature points in the non-single-chain structure are used as breakpoints to segment the boundary feature curve, thereby avoiding the appearance of non-single-chain structures in the boundary feature curve.

[0056] It should be noted that, in this embodiment of the invention, the side length of the three-dimensional pooling window under the corresponding pooling method is considered. Based on experience, the distance threshold is preset to... The values ​​can be adjusted according to the actual situation, and the embodiments of the present invention do not impose specific limitations.

[0057] Finally, for any two 3D point cloud data with different scanning directions, the DDTW algorithm is used to obtain the shortest alignment path between any two boundary feature curves in the two 3D point cloud data and the correspondence between feature points in the two boundary feature curves. For any one of the two boundary feature curves, it is denoted as the first target boundary feature curve, and the other boundary feature curve is denoted as the second target boundary feature curve. The number of feature points on the first target boundary feature curve that have a correspondence with the second target boundary feature curve is counted and denoted as the matching parameter. The number of feature points on the two boundary feature curves whose matching parameter is greater than a preset first parameter is denoted as the overmatch parameter between the two boundary feature curves. Based on the length of the shortest alignment path and the overmatch parameter between the two boundary feature curves, the consistency of the change trend between the two boundary feature curves is obtained, wherein the length of the shortest alignment path and the overmatch parameter are negatively correlated with the consistency of the change trend.

[0058] It should be noted that the DDTW (Derivative Dynamic Time Warping) algorithm is an existing sequence analysis algorithm, and therefore will not be described in detail in this embodiment of the invention.

[0059] It should also be noted that, in this embodiment of the invention, the first parameter is preset to 5 based on experience, and can be adjusted according to the actual situation. This embodiment of the invention does not impose any specific limitations.

[0060] As an optional embodiment, the specific method for calculating the consistency of the changing trends between any two boundary feature curves is as follows:

[0061]

[0062] in, Representing boundary characteristic curves and boundary feature curves The degree of consistency in the changing trends between them; Representing boundary characteristic curves and boundary feature curves Overmatch parameters between; Representing boundary characteristic curves and boundary feature curves The length of the shortest alignment path between them; This represents the sigmoid normalization function.

[0063] It should be noted that when the overmatch parameter between two boundary feature curves is smaller and the length of the shortest alignment path is shorter, the changing trends of the two boundary feature curves are more consistent, and the degree of consistency of the changing trends is greater.

[0064] Step S202: Under any pooling method, based on the consistency of the change trend, obtain the matching relationship between the boundary feature curves in the pooled point cloud data under any two scanning directions, and count the number of boundary feature curves with matching relationships in the 3D point cloud data of all scanning directions. Combined with the number of feature points in the 3D point cloud data of all scanning directions, calculate the pooling suitability and thus select the target point cloud data.

[0065] First, when the consistency of the change trend is greater than the preset consistency threshold, the corresponding second target boundary feature curve is used as the matching curve of the corresponding first target boundary feature curve. Under arbitrary pooling mode, the number of matching curves of arbitrary boundary feature curves in 3D point cloud data in arbitrary scanning direction is obtained and recorded as the matching quantity of the boundary feature curve. And the total number of feature points in all 3D point cloud data under arbitrary pooling mode is counted.

[0066] Then, based on the side length of the pooling window corresponding to any pooling method, the matching amount of all boundary feature curves in the 3D point cloud data of all scanning directions, and the total number of feature points in the 3D point cloud data of all scanning directions, the pooling suitability is calculated. The side length of the pooling window and the matching amount of all boundary feature curves in the 3D point cloud data of all scanning directions are positively correlated with the pooling suitability, while the total number of feature points in the 3D point cloud data of all scanning directions is negatively correlated with the pooling suitability.

[0067] As an optional embodiment, the specific calculation method for the pooling suitability is as follows:

[0068]

[0069] in, This indicates that the window's side length is... At that time, the appropriateness of the pooling method; This indicates that the window's side length is... At that time, it corresponds to the matching amount of all boundary feature curves in the 3D point cloud data of all scanning directions under the pooling method; This indicates that the window's side length is... At that time, it corresponds to the total number of feature points in the 3D point cloud data of all scanning directions under the pooling method; This indicates the side length of the 3D pooling window.

[0070] It should be noted that when the pooling window size is When the number of matching boundary feature curves and the total number of feature points in the 3D point cloud data of all scanning directions under the corresponding pooling method is larger, and the total number of feature points in the 3D point cloud data of all scanning directions under the corresponding pooling method is smaller, and the value of 'a' corresponding to the pooling window is larger, this pooling method can both ensure the correspondence between 3D point cloud data of different scanning directions and reduce the number of feature points used for analysis in the 3D point cloud data, making it the most suitable pooling method.

[0071] Finally, the pooling method that maximizes the pooling suitability among all pooling methods is obtained and denoted as the target pooling method. The pooled point cloud data obtained under the target pooling method is then denoted as the target point cloud data.

[0072] Thus, the target point cloud data is obtained through the above method.

[0073] Step S003: Filter out reference curves by utilizing the overall distribution level of the trend consistency corresponding to the boundary feature curves with matching relationships in different target point cloud data, and filter out reference point clouds by the number and length of reference curves in the target point cloud data; adjust the position of feature points in the reference point cloud based on the matching relationship between the boundary feature curves.

[0074] It should be noted that the ranging and angle measurement accuracy of a 3D laser scanner may cause distortion of the positions of certain points in the point cloud, making it difficult to register 3D point clouds of an object acquired from different angles. To simplify the 3D point cloud registration process and reduce the need for adjustments to the positions in different 3D point clouds, the 3D point cloud with the highest degree of matching with the boundary feature curves among multiple 3D point clouds should be selected as the reference 3D point cloud.

[0075] Specifically, in step S301, reference curves are selected by utilizing the overall distribution level of the trend consistency corresponding to the boundary feature curves with matching relationships in different target point cloud data, and reference point clouds are selected by the number and length of reference curves in the target point cloud data.

[0076] First, based on the overall distribution level of the trend consistency of the boundary feature curves that have matching relationships in different target point cloud data, the true conformity of the boundary feature curves is calculated.

[0077] As a preferred embodiment, the specific method for calculating the true conformity of the boundary feature curve is as follows:

[0078] For target point cloud data in any scanning direction, the number of target point cloud data corresponding to any boundary feature curve in the target point cloud data in all other scanning directions is obtained, and this number is denoted as the matching inclusion of the boundary feature curve. The target point cloud data in any scanning direction is denoted as the first point cloud data, and any boundary feature region in the first point cloud data is denoted as the first boundary feature curve. The maximum trend consistency of all matching curves of the first boundary feature curve in any target point cloud data other than the first point cloud data is obtained, and this maximum trend consistency of the first boundary feature curve in any target point cloud data other than the first point cloud data is denoted as the consistency parameter of the first boundary feature curve. Based on the matching inclusion and consistency parameter of the arbitrary boundary feature curve, the true conformity of the boundary feature curve is calculated, wherein both the matching inclusion and the consistency parameter are positively correlated with the true conformity.

[0079] As an optional embodiment, the specific method for calculating the true conformity is as follows:

[0080]

[0081] in, Indicates the first In the target point cloud data of the scanning direction, the first The true conformity of each boundary characteristic curve; Indicates the first In the target point cloud data of the scanning direction, the first The matching inclusion of each boundary feature curve; Indicates the first In the target point cloud data of the scanning direction, the first Consistent parameters of each boundary characteristic curve; This represents the sigmoid normalization function.

[0082] It should be noted that when the first In the target point cloud data of the scanning direction, the first The larger the matching inclusion of a boundary feature curve, the more 3D point clouds there are when the boundary feature curve has a matching relationship with other 3D point clouds. When the average value of the maximum value of the trend consistency in different 3D point clouds is larger, the matching degree of the boundary feature curve c in 3D point clouds at other angles is greater, and the possibility of distortion problems is smaller.

[0083] Then, when the actual conformity is greater than the preset second parameter, the corresponding boundary feature curve is recorded as the reference curve.

[0084] It should be noted that the second parameter is preset to 0.7 based on experience, and can be adjusted according to the actual situation. This embodiment of the invention does not impose specific limitations.

[0085] Finally, the number of reference curves contained in the target point cloud data in any scanning direction, and the number of feature points contained in all reference curves in the target point cloud data are obtained; combined with the number of reference curves in the target point cloud data in the scanning direction, and the proportion of feature points contained in all reference curves in the target point cloud data to the total number of feature points in all target point cloud data, the reference coefficient of the target point cloud data in the scanning direction is calculated, wherein the number of reference curves and the proportion of feature points are both positively correlated with the reference coefficient; the target point cloud data with the largest reference coefficient among all target point cloud data in the scanning directions is taken as the reference point cloud.

[0086] As an optional embodiment, the specific calculation method for the reference coefficient is as follows:

[0087]

[0088] in, Indicates the first Reference coefficients for target point cloud data in each scanning direction; Indicates the first The number of reference curves contained in the target point cloud data in each scanning direction; Indicates the first The number of feature points contained in all reference curves in the target point cloud data of each scanning direction; This indicates the number of feature points contained in all reference curves across all target point cloud data.

[0089] It should be noted that the reference coefficient is used to describe the reference value of the corresponding target point cloud data when used for subsequent point cloud registration; for target point cloud data in any scanning direction, the more reference curves the target point cloud data contains, and the higher the proportion of feature points on the object's reference curves, the better. The larger the value, the smaller the difference between the boundary feature curve in the target point cloud data and the boundary of the actual scanned object. Therefore, the target point cloud data has greater reference value as a reference point cloud for subsequent point cloud registration.

[0090] Step S302: Adjust the position of feature points in the reference point cloud based on the matching relationship between boundary feature curves.

[0091] It should be noted that the reference point cloud itself may also have some positional distortion. In order to make the position of each point in the reference point cloud more consistent with the actual object, the feature points in different boundary feature curves should be restored first, and the position of the points outside the boundary feature curves should be restored by using the distance change of the boundary feature curves during restoration as a reference, so as to make the position of each point in the reference point cloud more referential.

[0092] First, obtain the feature points in the boundary feature curve and the corresponding matching curve that have a matching relationship in any reference point cloud, and denot them as the matching points of the feature points in the boundary feature curve and the matching points in the matching curve. Obtain the Euclidean distance and Euler angle between any feature point in the boundary feature curve and the matching point in the corresponding matching curve. Obtain the distance between any feature point and the matching point, and denot it as the matching distance of the feature point. Also obtain the Euler angle of the connecting path, and denot it as the matching angle of the feature point. Calculate the difference between the matching distances of the feature point and other feature points on the same boundary feature curve, and the sum of the differences of the matching angles at different angles. Based on the difference between the matching distances of the feature point and all other feature points on the same feature curve, and the sum of the differences of the corresponding matching angles at different angles, obtain the relationship performance coefficient of the feature point.

[0093] As an optional embodiment, the specific calculation method for the relational expression coefficient is as follows:

[0094]

[0095] in , Indicates the first The first boundary characteristic curve The coefficients representing the relationship between each feature point; Indicates the first The first boundary characteristic curve The feature point and the first The difference in matching distance between feature points; Indicates the first The number of feature points in each boundary feature curve; This represents the sigmoid normalization function.

[0096] It should be noted that when a feature point is on the same boundary feature curve as other feature points... and The smaller the cumulative difference of the product, the greater the consistency of the correspondence between feature points, and the more correct their positions. For the same pair of boundary feature curves, when comparing a single feature point on one boundary feature curve with other feature points on the same boundary feature curve, the more consistent the matching distance between the feature point and its matching point and the matching distance between other feature points on the same boundary feature curve and their corresponding matching points, and the more consistent the Euler angles, the greater the consistency of the correspondence between the feature point on the boundary feature curve and the feature point on the matching curve, and the more correct the position of the feature point on the boundary feature curve.

[0097] Then, for any boundary feature curve of any reference point cloud, the number of feature points in the boundary feature curve whose relational expression coefficient is greater than a preset third parameter is obtained, and denoted as the first number of the boundary feature curve. Based on the first number of the boundary feature curve and the degree of consistency between the changing trend of the boundary feature curve and the matching curve, the adjustment coefficient between the boundary feature curve and the matching curve is obtained, wherein the first number and the degree of consistency between the changing trend are positively correlated with the adjustment coefficient.

[0098] As an optional embodiment, the specific calculation method for the adjustment coefficient is as follows:

[0099]

[0100] in, Indicates the first The boundary feature curve and the corresponding first boundary feature curve Adjustment coefficients between matching curves Indicates the first The first number of boundary characteristic curves; Indicates the first The boundary feature curve and the corresponding first boundary feature curve The degree of consistency in the changing trends among the matching curves.

[0101] It should be noted that when the boundary feature point i in the boundary feature curve of the reference point cloud for point cloud registration corresponds to... Compared to other points g When the value is at its maximum, the position of boundary feature point i in the boundary feature curve of the reference point cloud is correctly determined. When the value is not at its maximum, the degree of consistency with the trend of the boundary feature curve in the reference point cloud registered with the point cloud is considered. Larger, and corresponding The larger curve serves as a reference for the positional changes of the boundary feature point i.

[0102] Finally, the maximum value among the adjustment coefficients of the boundary feature curve and all corresponding matching curves is obtained. The matching point in the matching curve corresponding to the maximum adjustment coefficient is taken as the reference point of the corresponding feature point in the boundary feature curve. According to the relative positional relationship between the reference points in the matching curves corresponding to the boundary feature curve, the position of the feature point in the boundary feature curve is adjusted so that the relative positional relationship between the feature points in the boundary feature curve is the same as the relative positional relationship between the reference points in the corresponding matching curve. The Euler angle and the distance before and after the position change of the feature point in the boundary feature curve are recorded as the adjusted Euler angle and the adjusted distance, respectively.

[0103] Thus, the adjusted feature points, their adjusted Euler angles, and their adjusted distances are obtained using the methods described above.

[0104] Step S004: Merge adjacent feature points based on the changes before and after feature point position adjustment to obtain several regions with consistent adjustment trends. Use the overall adjustment of feature points in the regions with consistent adjustment trends to move and adjust data points other than feature points in the reference point cloud. After adjusting the positions of all data points in all reference point clouds, a new reference point cloud is obtained.

[0105] It should be noted that after adjusting the position of the boundary feature curves in the reference point cloud, the positions of the points within the boundary curves should be restored. Since the distortion varies in different regions, regions with consistent distortion are divided based on the restored performance. The positions of the corresponding points are then adjusted according to the relationship between the reference point positions and the positions of the divided distorted regions.

[0106] Specifically, firstly, for the reference point cloud after adjusting the position of feature points in the boundary feature curve, the absolute value of the difference in adjustment distance between adjacent feature points in the reference point cloud and the sum of the absolute values ​​of the difference in adjustment Euler angle are obtained and recorded as the first difference and the second difference, respectively. Based on the first difference and the second difference, the feasibility of merging adjacent feature points is obtained. The first difference and the second difference are both negatively correlated with the merging feasibility. When the merging feasibility is greater than or equal to the preset fourth parameter, the corresponding two feature points are merged. After merging all adjacent feature points with merging feasibility greater than or equal to the fourth parameter, several regions with consistent adjustment trends are obtained.

[0107] As an optional embodiment, the specific method for calculating the feasibility of the merger is as follows:

[0108]

[0109] in, Indicates the first The feature point and the first Feasibility of merging feature points; Indicates the first The feature point and the first The first difference between the feature points; Indicates the first The feature point and the first The second difference between the feature points; This represents the sigmoid normalization function.

[0110] It should be noted that after adjusting the position of the feature points, when the difference in adjustment distance and adjustment angle between adjacent feature points in the obtained reference point cloud is small, it indicates that the adjustment trends of the two feature points are similar. Therefore, in this embodiment of the invention, the feasibility of merging two feature points is used to determine whether adjacent feature points can be classified into the same adjustment trend region.

[0111] Then, for any region with a consistent adjustment trend, the feature point with the largest adjustment distance within that region is identified and designated as the main feature point. The adjustment distance of the main feature point is taken as the adjustment length of the region with the consistent adjustment trend, and the adjustment Euler angle of the main feature point is taken as the adjustment scanning direction of the region with the consistent adjustment trend. Data points other than feature points in the reference point cloud are designated as non-feature points. The region with the closest consistent adjustment trend to any non-feature point is identified and designated as the target region of the non-feature point. For any non-feature point, the midpoint of the boundary feature curve closest to the non-feature point in the target region is identified, and the region passing through the midpoint and the scanning direction is determined. The straight line along the adjustment scanning direction of the target area is taken as the adjustment trend line of the target area. The angle between the line connecting the non-feature point and the midpoint and the adjustment trend line is recorded as the first adjustment parameter of the non-feature point. The minimum distance between the non-feature point and the adjustment trend line is recorded as the second adjustment parameter of the non-feature point. The adjustment influence of the non-feature point is obtained according to the first adjustment parameter and the second adjustment parameter. Combining the adjustment length of the target area of ​​the non-feature point and the adjustment influence of the non-feature point, the moving distance of the non-feature point along the adjustment scanning direction of the corresponding target area is obtained.

[0112] As an optional embodiment, the specific calculation method for the moving distance of the non-feature point along the adjusted scanning direction of the corresponding target region is as follows:

[0113]

[0114] in, Indicates the first The distance a non-feature point moves along the adjusted scanning direction of the corresponding target area; Indicates the first Adjustment length of the target region corresponding to each non-feature point; Indicates the first The impact of adjusting non-feature points.

[0115] The specific method for obtaining the adjustment influence of the non-feature point based on the first adjustment parameter and the second adjustment parameter further includes: both the first adjustment parameter and the second adjustment parameter are negatively correlated with the adjustment influence of the non-feature point.

[0116] As an optional embodiment, the specific calculation method for the adjustment influence of the non-feature points is as follows:

[0117]

[0118] in, Indicates the first The degree of influence of the adjustment of each non-feature point; Indicates the first The first adjustment parameter for each non-feature point; Indicates the first The second adjustment parameter for each non-feature point; This represents the sigmoid normalization function.

[0119] It should be noted that the greater the adjustment effect, the longer the movement distance of the ordinary point p should be. This is because the difference in angle between the connected line and the adjustment trend at different Euler angles... The smaller, and the distance The smaller the value, the greater the influence of the trend of change in this region on the position of the ordinary point p.

[0120] Finally, all non-feature points in the reference point cloud are moved along the adjustment scanning direction of the corresponding target area according to the corresponding moving distance, and the feature points in the reference point cloud after position adjustment are merged according to the merging feasibility to obtain a new reference point cloud.

[0121] Thus, a new reference point cloud is obtained by adjusting and moving the positions of all data points in the reference point cloud using the above method.

[0122] Step S005: Register and stitch all new reference point clouds.

[0123] Specifically, firstly, all adjusted reference point clouds are registered and stitched together using the corresponding boundary feature curves to obtain the complete point cloud data of the object. Through the above operations, the point cloud data from the 3D laser scanner is processed to obtain the adjustment methods for data points in different point cloud data and the final stitched point cloud.

[0124] Then, the obtained analysis data is transmitted to the database for corresponding storage. The adjustment distances and Euler angles of feature points, as well as the adjustment distances and scanning directions of non-feature points in different reference point clouds, are recorded using SQL query statements, and then visualized in the form of tables.

[0125] By following the steps above, the calibration of the 3D point cloud data of the 3D laser scanner is completed.

[0126] An error calibration system for a 3D laser scanner includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements steps S001 to S005 of the error calibration method for a 3D laser scanner.

[0127] Furthermore, in an alternative embodiment, the memory described above may include read-only memory and random access memory, and provide instructions and data to the processor. The memory may also include non-volatile random access memory. For example, the memory may also store device type information.

[0128] The memory can be volatile or non-volatile, or may include both. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which serves as an external cache. Many forms of RAM are available by way of example, but not limitation. Examples include Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced Synchronous DRAM (ESDRAM), Sync Link DRAM (SLDRAM), and Direct Rambus RAM (DRRAM).

[0129] The aforementioned processor can be a Central Processing Unit (CPU), or other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. General-purpose processors can be microprocessors or any conventional processor. It is worth noting that the processor can be a processor supporting Advanced Reduced Instruction Set Machines (ARM) architecture.

[0130] This embodiment analyzes the changing trends between boundary feature curves to achieve precise matching of feature points, thereby optimizing the subsequent registration and stitching process of point cloud data. Furthermore, by utilizing the matching relationship of boundary feature curves, a reference curve consistent with the overall trend is selected, improving the selection accuracy of the reference point cloud data and enabling effective adjustment of feature point positions. By adjusting feature point positions and merging regions with consistent trends, the matching accuracy of adjacent feature points can be effectively improved, resulting in a more accurate final reference point cloud and enhancing the registration effect of the point cloud data. Further, by adjusting the positions of data points in all reference point clouds, global consistency of all data points is ensured, leading to a more realistic and accurate new reference point cloud, thus improving the accuracy of the 3D point cloud data acquired by the 3D laser scanner.

[0131] It should be noted that the embodiments used in this example The model is only used to represent negative correlations and the results of the constraint model output are in Within this range, in specific implementations, other models with the same purpose can be substituted; this embodiment is merely an example. The description will be based on a model, without making specific limitations on it. This refers to the input of the model.

[0132] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for error calibration of a three-dimensional laser scanner, characterized in that, The method comprises the following steps: scanning the object by a three-dimensional laser scanner in several scanning directions to obtain three-dimensional point cloud data in each scanning direction; constructing several three-dimensional pooling windows of different sizes to respectively pool any three-dimensional point cloud data, obtaining pooled point cloud data and several boundary feature curves composed of several feature points in the pooled point cloud data, analyzing the consistent degree of the change trend between the boundary feature curves to obtain a matching relationship between the boundary feature curves, and screening the pooled point cloud data based on the matching relationship between the boundary feature curves in the three-dimensional point cloud data in all scanning directions to obtain target point cloud data; screening a reference curve by using the overall distribution level of the trend consistency degree corresponding to the boundary feature curves having the matching relationship in different target point cloud data, and screening a reference point cloud by the number and length of the reference curve in the target point cloud data; and adjusting the positions of the feature points in the reference point cloud based on the matching relationship between the boundary feature curves. merging adjacent feature points by the change before and after the position adjustment of the feature points to obtain several adjustment trend consistent regions, and moving and adjusting data points other than the feature points in the reference point cloud by the overall adjustment of the feature points in the adjustment trend consistent regions, and obtaining new reference point clouds by the position adjustment of all data points in all reference point clouds. registering and splicing all the new reference point clouds. The method for moving and adjusting the data points other than the feature points in the reference point cloud by the overall adjustment of the feature points in the adjustment trend consistent regions comprises the following steps: for any adjustment trend consistent region, obtaining a feature point with the largest adjustment distance in the adjustment trend consistent region, denoted as a main feature point, taking the adjustment distance of the main feature point as the adjustment length of the adjustment trend consistent region, taking the adjustment Euler angle of the main feature point as the adjustment scanning direction of the adjustment trend consistent region, taking data points other than the feature points in the reference point cloud as non-feature points, obtaining a target region of any non-feature point closest to the adjustment trend consistent region, denoted as the target region of the non-feature point, obtaining the midpoint of the boundary feature curve closest to the non-feature point in the target region, and taking a straight line passing through the midpoint and having the adjustment scanning direction of the target region as the adjustment trend line of the target region, obtaining the included angle between the connecting line of the non-feature point and the midpoint and the adjustment trend line as the first adjustment parameter of the non-feature point, obtaining the minimum distance between the non-feature point and the adjustment trend line as the second adjustment parameter of the non-feature point, obtaining the adjustment influence degree of the non-feature point according to the first adjustment parameter and the second adjustment parameter, and obtaining the moving distance of the non-feature point along the adjustment scanning direction of the corresponding target region by combining the adjustment length of the target region of the non-feature point and the adjustment influence degree of the non-feature point.

2. The method for error calibration of a three-dimensional laser scanner according to claim 1, wherein, The method for constructing several three-dimensional pooling windows of different sizes to respectively pool any three-dimensional point cloud data, obtaining pooled point cloud data and several boundary feature curves composed of several feature points in the pooled point cloud data comprises the following steps: A preset edge length size is a three-dimensional pooling window, and the three-dimensional point cloud data is slidingly traversed using the three-dimensional pooling window. In the sliding traversal process, the three-dimensional point cloud data is down-sampled by a max-pooling operation. The max-pooling operation of the three-dimensional pooling window with an arbitrary edge length size is taken as a pooling manner, and the point cloud data after pooling and a plurality of feature points contained in the point cloud data after pooling under the arbitrary pooling manner are obtained. The edge length of the three-dimensional pooling window is , and the size of the three-dimensional pooling window is increased in an interval with as a step size. The sliding compensation of the three-dimensional pooling window in the sliding traversal process is , wherein , , and are respectively a preset first edge length parameter, a second edge length parameter, a first step size parameter and a second step size parameter. In any pooling manner, the distance between the feature points is obtained, and the feature points with a distance less than a preset distance threshold are connected to each other to obtain a plurality of boundary feature curves under the corresponding pooling manner.

3. The method for error calibration of a three-dimensional laser scanner according to claim 1, wherein, The specific method for obtaining the consistency degree of the change trend between the boundary feature curves is: For any two three-dimensional point cloud data in different scanning directions, the shortest alignment path between any two boundary feature curves in the two three-dimensional point cloud data and the corresponding relationship between the feature points in the two boundary feature curves are obtained by the DDTW algorithm. For any one of the two boundary feature curves, denoted as a first target boundary feature curve, the other boundary feature curve is denoted as a second target boundary feature curve. The number of feature points in the first target boundary feature curve that have a corresponding relationship with the feature points in the second target boundary feature curve is counted and denoted as a matching parameter. The number of feature points in the two boundary feature curves whose matching parameter is greater than a preset first parameter is denoted as an over-matching parameter between the two boundary feature curves. The length of the shortest alignment path and the over-matching parameter are negatively correlated with the consistency degree of the change trend between the two boundary feature curves.

4. The method for error calibration of a three-dimensional laser scanner according to claim 2, wherein, The specific method for screening the target point cloud data from the pooled point cloud data based on the matching relationship between the boundary feature curves in the three-dimensional point cloud data in all scanning directions is: When the consistency degree of the change trend is greater than a preset consistency threshold, the corresponding second target boundary feature curve is taken as the matching curve of the corresponding first target boundary feature curve. In any pooling manner, the number of matching curves of any boundary feature curve in the three-dimensional point cloud data in any scanning direction is obtained and denoted as the matching amount of the boundary feature curve. The total number of feature points in all three-dimensional point cloud data in any pooling manner is counted. The pooling suitability in the pooling manner is calculated according to the side length of the corresponding pooling window in any pooling manner, the matching amount of all boundary feature curves in the three-dimensional point cloud data in all scanning directions, and the total number of feature points in the three-dimensional point cloud data in all scanning directions. The side length of the pooling window and the matching amount of all boundary feature curves in the three-dimensional point cloud data in all scanning directions are positively correlated with the pooling suitability, and the total number of feature points in the three-dimensional point cloud data in all scanning directions is negatively correlated with the pooling suitability. The pooling manner corresponding to the maximum pooling suitability in all pooling manners is obtained and denoted as a target pooling manner. The pooled point cloud data obtained in the target pooling manner is denoted as a target point cloud data.

5. The method for error calibration of a three-dimensional laser scanner according to claim 4, wherein, The specific method for screening the reference curve using the overall distribution level of the trend consistency degree corresponding to the boundary feature curves having a matching relationship in different target point cloud data is: For the target point cloud data of any scanning direction, the number of corresponding target point cloud data when any boundary feature curve in the target point cloud data has a matching curve in all other target point cloud data is obtained, and is recorded as a matching inclusion amount of the boundary feature curve; the target point cloud data of any scanning direction is recorded as first point cloud data, and any boundary feature region in the first point cloud data is recorded as a first boundary feature curve; the maximum value of the trend consistency degree of all matching curves of the first boundary feature curve in any target point cloud data except the first point cloud data is obtained, and is recorded as the maximum trend consistency degree of the first boundary feature curve in the any target point cloud data except the first point cloud data; the average value of the maximum trend consistency degree of the first boundary feature curve in the any target point cloud data except the first point cloud data is obtained, and is recorded as a consistency parameter of the first boundary feature curve; the real compliance degree of the boundary feature curve is calculated according to the matching inclusion amount and the consistency parameter of any boundary feature curve, wherein the matching inclusion amount and the consistency parameter are positively correlated with the real compliance degree; When the real compliance degree is greater than a preset second parameter, the corresponding boundary feature curve is recorded as a reference curve.

6. The method for error calibration of a 3-D laser scanner according to claim 1, wherein, The reference point cloud is screened out by the number and length of the reference curves in the target point cloud data, including the following specific method: The number of reference curves contained in the target point cloud data of any scanning direction and the number of feature points contained in all reference curves in the target point cloud data are obtained; the reference coefficient of the target point cloud data of the scanning direction is calculated in combination with the number of reference curves in the target point cloud data of the scanning direction and the proportion of the number of feature points contained in all reference curves in the target point cloud data in the number of feature points in all target point cloud data, wherein the number of reference curves and the proportion of the number of feature points are positively correlated with the reference coefficient; the target point cloud data with the maximum reference coefficient in all target point cloud data of all scanning directions is taken as the reference point cloud.

7. The method for error calibration of a three-dimensional laser scanner according to claim 4, wherein, The position of the feature point in the reference point cloud is adjusted based on the matching relationship between the boundary feature curves, including the following specific method: The maximum value of the adjustment coefficient of the boundary feature curve and all corresponding matching curves is obtained, and the matching point in the matching curve corresponding to the maximum adjustment coefficient is taken as the reference point of the corresponding feature point in the boundary feature curve; the position of the feature point in the boundary feature curve is adjusted according to the relative position relationship between the reference points in the corresponding matching curve of the boundary feature curve, so that the relative position relationship between the feature points in the boundary feature curve is the same as the relative position relationship between the reference points in the corresponding matching curve, and the Euler angle before and after the position change of the feature point in the boundary feature curve and the distance before and after the position change are recorded, respectively, as the adjustment Euler angle and the adjustment distance.

8. The method for error calibration of a three-dimensional laser scanner according to claim 7, wherein, The adjacent feature points are merged by the change before and after the position adjustment of the feature points to obtain a plurality of adjustment trend consistent regions, including the following specific method: For the reference point cloud after adjusting the feature point positions in the boundary feature curve, an accumulated sum of absolute values of difference of adjustment distances between adjacent feature points in the reference point cloud and an accumulated sum of absolute values of difference of adjustment Euler angles are obtained, denoted as a first difference and a second difference respectively, a merging feasibility between adjacent feature points is obtained according to the first difference and the second difference, the first difference and the second difference are negatively correlated with the merging feasibility, when the merging feasibility is greater than or equal to a fourth preset parameter, the corresponding two feature points are merged, after merging processing of all adjacent feature points with the merging feasibility greater than or equal to the fourth parameter, a plurality of adjustment trend consistent regions are obtained.

9. A system for error calibration of a three-dimensional laser scanner, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that, The processor, when executing the computer program, implements the steps of the error calibration method for a three-dimensional laser scanner according to any one of claims 1-8.

Images