Nonlinear registration optimization method for fastener point cloud

Abstract

Disclosed in the present invention is a nonlinear registration optimization method for a fastener point cloud. The method comprises the following steps: S1, collecting original data; S2, preprocessing the data; S3, converting frame images into a deformation map; S4, performing registration between the frame images; and S5, simplifying corrected point cloud data. The method in the present invention improves the data accuracy and confidence level of an original point cloud, and provides more reliable data input for subsequent point cloud processing and advanced understanding processes, thereby ensuring the achievement rate of point cloud processing objectives; and the point cloud optimization process in the present invention has high robustness, and effective features of original point cloud data are retained to the greatest extent while noise points and outlier points are eliminated, thereby achieving high detail retainability. Moreover, the nonlinear optimization process avoids falling into the trap of local optimization, achieving high computational efficiency, strong universality, and high potential for popularization and application.

Description

Nonlinear registration optimization method for fastener point cloud TECHNICAL FIELD

[0001] The present application belongs to the field of rapid comprehensive detection of rail transit, and particularly relates to a nonlinear registration optimization method for fastener point cloud. BACKGROUND

[0002] The supporting fasteners of a track are an important component of the entire rail transit project, and their state directly affects the operational safety of trains. After long-term operation and maintenance of a rail transit line, the service state of the fasteners changes, affecting the geographical spatial position and smoothness requirements of the line. With the development of high-speed railways and urban rail transit, the demand for detecting abnormal states of fasteners is increasing. However, in the process of fastener detection, the point cloud data, which is the most critical input, often deviates from the general redundancy range due to environmental noise, hardware distortion, human error, partial failure of fasteners, and complex conditions in the target area, directly affecting the accuracy of fastener state evaluation and the timeliness of maintenance decisions, and further affecting railway operation safety. It is urgent to correct and optimize the collected fastener point cloud data.

[0003] There are mainly three types of existing point cloud optimization algorithms:

[0004] (1) Point cloud optimization algorithm based on mathematical statistical concepts;

[0005] (2) Point cloud optimization algorithm based on features in each dimension;

[0006] (3) Point cloud optimization algorithm based on spatial domain state.

[0007] The above methods have the following problems:

[0008] (1) The optimization dimension of the algorithm is single, the description coverage is weak, and additional errors or mistakes are easily introduced;

[0009] (2) The coupling degree of the algorithm process with the target point cloud is low, and the specificity is poor;

[0010] (3) The solving process is repeated, the computing power is wasted, the time efficiency is low, and the workload is large, etc.

[0011] In view of the above problems and weaknesses, it is urgent to develop a nonlinear registration optimization technology for fastener point cloud, which can comprehensively, efficiently and accurately process and optimize the collected fastener point cloud data, and provide more reliable and credible data input for subsequent point cloud understanding and advanced processing. SUMMARY

[0012] To solve the problems in the prior art, the application provides a nonlinear registration optimization method for fastener point clouds, which can comprehensively, efficiently and accurately process and optimize the collected fastener point cloud data, and provide more reliable and credible data input for subsequent point cloud understanding and advanced processing.

[0013] To this end, the application adopts the following technical solutions:

[0014] A nonlinear registration optimization method for fastener point clouds comprises the following steps:

[0015] S1, original data acquisition: using a collection device to collect point cloud data containing spatial coordinates and timestamp information in the active axial direction, the depth axial direction and the offset axial direction; the active axial direction is the tangent direction of the curve of the movement of the collection device, and the active axial direction, the depth axial direction and the offset axial direction are perpendicular to each other;

[0016] S2, data preprocessing: dividing the point cloud data collected in S1 into a plurality of frame images according to the timestamp information in the form of spatial slices along the time axis;

[0017] S3, converting the frame images into deformation maps: S i ={p i ,χ i};

[0018] wherein, S i is the frame image of the i-th frame, which is composed of a point set p i and an edge set χ i ; n i is the total number of points in the frame image of the i-th frame, m i is the total number of edges in the frame image of the i-th frame, i is the number of frame images, i∈[1,I], and I is the total number of frame images;

[0019] S4, registration between frame images:

[0020] First, a registration mapping relationship E wicp between frame images is constructed, and has: E wicp =min(E pr +β wicp E nreg +γ wicp E lor );

[0021] wherein: E pr is an alignment description item; E nreg is a regularization description item; E lor is a local transformation item; β wicp and γ wicp are index weights, and the β wicp and the γwicp The value of the difference between the frame images, and the value of β wicp and γ wicp range from (0, 1),

[0022] Then, the best registration relationship is obtained according to the registration mapping relationship E wicp , and all the frame images obtained in S2 are registered according to the best registration relationship, so as to obtain the modified point cloud data.

[0023] S5, simplifying the modified point cloud data, comprising the following steps:

[0024] S51, establishing an initial ellipsoid space:

[0025] First, a point p tag in the modified point cloud data obtained in S4 is taken as the center of the ellipsoid space; the initial length of the long axis of the ellipsoid space is twice the distance between the center of the ellipsoid space and the nearest neighbor point in the projection of the offset axis, and the initial length of the short axis of the ellipsoid space is twice the distance between the center of the ellipsoid space and the nearest neighbor point in the projection of the depth axis.

[0026] Wherein, the nearest neighbor point is the point in the modified point cloud data with the smallest Euclidean distance from the center of the ellipsoid space.

[0027] Then, the direction of the long axis of the ellipsoid is parallel to the offset axis, and the direction of the short axis is parallel to the depth axis, and the two end points of the long axis are O au and O ad , respectively; the two end points of the short axis are O bu and O bd , respectively; and an initial ellipsoid space is obtained.

[0028] Finally, an ellipsoid space set is constructed, and the initial ellipsoid space is added as the first element in the set.

[0029] S52, ellipsoid space diffusion: the ellipsoid spaces in the ellipsoid space set are added or reconstructed, and the S4 modified point cloud data covered by the ellipsoid spaces are simplified.

[0030] S53, repeating step S52 until the ellipsoid spaces in the ellipsoid space set cover all the S4 modified point cloud data, and the obtained point cloud is output as the high-quality point cloud data after nonlinear registration.

[0031] The alignment description item E pr is calculated by the following method:

[0032] Wherein, f wel(x) is a Welsch function; is a point in the frame image of the i-th frame, y is the number of the point in the frame image of the i-th frame, y ∈ [1, n i ]; is the nearest target point in the frame image to be registered of the i-th frame, the frame to be registered is set as the j-th frame, j ∈ [1, I]; c wel is a constant greater than 0 and making the f wel monotonically increasing on the positive half axis.

[0033] The regularization description item E nreg is calculated by the following method:

[0034] wherein, c' wel is the updated setting constant item in the Welsch function, c' wel is a constant greater than 0 and making the f wel monotonically increasing on the positive half axis. denotes the set of neighboring points of the point in the frame image to be registered, z is the number of the point in the frame image of the j-th frame, z ∈ [1, n j ], n j is the total number of points in the frame image of the j-th frame. is the transformation form of each point in the deformation graph considering the spatial cross coupling.

[0035] The regularization description item is calculated by the following method:

[0036] wherein, is the affine rotation matrix of the embedded deformation graph; and are the affine translation vectors of the embedded deformation graph corresponding to the i-th frame and the j-th frame respectively.

[0037] The local transformation item E lor is calculated by the following method:

[0038] wherein, η proj is a modification factor considering the local transformation intensity, the value range of which is (0, 1); the operator symbol ||·||F F is the F norm solution considering the differential local rigidity state.

[0039] S52 specifically comprises the following steps: S521, for each ellipsoid space in the ellipsoid space set, a point p k is randomly determined outside the ellipsoid space, the p kFor each point p in the modified point cloud data obtained in S4 k S522 is executed when it satisfies the following formula, otherwise S523 is executed,

[0040] wherein, is a normal vector of the plane formed by the long axis and the short axis of the ellipse and is a normal vector of the plane formed by the long axis and the short axis of the ellipse and dis is the distance threshold of the point p k to the projection plane, ξ ang is the vector deviation threshold; the values of the ξ dis and ξ ang are determined according to the fastener to be registered, and the projection plane is the plane on which the p k corresponds to the center of the ellipse of the ellipsoid space k corresponds to the plane on which the corresponding frame is located;

[0041] S522, reconstructing the ellipsoid space:

[0042] integrating the point p k into the corresponding ellipsoid space, and the center of the ellipse O k remains unchanged, the length of the long axis of the corresponding ellipsoid space is changed to a' el , and the length of the short axis is changed to b ′ el , to obtain the reconstructed ellipsoid space, which has:

[0043] wherein, O kau is the long axis endpoint of the ellipsoid space with O k as the center, and O kbu is the short axis endpoint of the ellipsoid space with O k as the center;

[0044] all the point cloud data covered in the reconstructed ellipsoid space are changed to the center of the ellipse point O k , the two endpoints of the long axis of the reconstructed ellipsoid space, the two endpoints of the short axis of the reconstructed ellipsoid space, and the p k ;

[0045] Step S53 is executed;

[0046] S523, constructing an ellipsoid space:

[0047] First, the diffusion direction of the ellipsoid space is determined according to the position of the point p k and the position of the center of the ellipse O k ;

[0048] Then, the nearest neighbor point p k of the point p w; the p w An additional ellipsoid space is added to the ellipsoid space set, and step S53 is performed. k The length of the ellipsoid long axis of the additional ellipsoid space is the length of the projection of the ellipsoid long axis of the ellipsoid space. k The length of the ellipsoid short axis of the additional ellipsoid space is the length of the projection of the ellipsoid short axis of the ellipsoid space.

[0049] The additional ellipsoid space is added to the ellipsoid space set, and step S53 is performed.

[0050] The diffusion direction in S523 is determined by the following formula:

[0051] Wherein, qua(p k ) represents the quadrant where the point p k is located.

[0052] Preferably, the collection device in S1 is a self-moving rail transit mobile three-dimensional scanning system.

[0053] Compared with the prior art, the present application has the following beneficial effects:

[0054] 1. The present application is directed to the original point cloud quality problem which is ignored in the current fastener evaluation work, and a nonlinear registration optimization algorithm is proposed, the data correction process of the point cloud is quantized into three types of description items, and the data accuracy and confidence of the original point cloud are further improved, which provides more reliable data input for the subsequent point cloud processing and advanced understanding process, and further ensures the realization rate of the point cloud processing target.

[0055] 2. The method of the present application combines the time and space continuity of the collected point cloud data, and focuses on local difference and global uniformity, detail rigidity distinction and overall non-rigid structure, and proposes a nonlinear target optimization equation using three types of description items, which further improves the accuracy, coverage and confidence of the optimization process.

[0056] 3. The method of the present application designs an ellipsoid containing space, realizes point cloud simplification and optimization in the form of ellipse diffusion, further improves the availability, simplicity and description of the point cloud data, and meets the needs of subsequent point cloud processing and advanced understanding process; the point cloud optimization process of the present application has high robustness, and on the basis of removing noise points and random points, the effective features of the original point cloud data are preserved as much as possible, and the detail retention is strong. At the same time, the nonlinear optimization process avoids falling into the local optimization trap, has high calculation efficiency, strong universality, and high application value. BRIEF DESCRIPTION OF DRAWINGS

[0057] FIG. 1 is a flowchart of the present application.

[0058] Figure 2 is a schematic diagram of point cloud data composed of each subdivided frame data in the data preprocessing process of the present application;

[0059] Figure 3 is a schematic diagram of an ellipsoid space set of the present application. DETAILED DESCRIPTION

[0060] The technical solutions of the present application are described in detail below in combination with the drawings and examples.

[0061] In the process of collecting digital point cloud, a point cloud coordinate system is defined, which includes three coordinate axes, namely depth axis, offset axis and active axis, wherein the active axis is the tangent direction of the curve of the motion of the collection system, the depth axis and the offset axis are perpendicular to the active axis, and the depth axis and the offset axis are also perpendicular to each other.

[0062] As shown in Figure 1, the non-linear registration optimization method of the fastener point cloud of the present application includes the following steps:

[0063] S1, original data collection: using a self-moving rail transit mobile three-dimensional scanning system, or using other collection systems and devices to collect and obtain point cloud data containing spatial coordinates and time stamp information under the aforementioned point cloud coordinate system.

[0064] S2, data preprocessing: according to the time stamp information obtained in S1, the complete point cloud is divided into several frame images in the form of spatial slices according to the time axis, and the plane composed of the depth axis and the offset axis is the plane of the point cloud slice as shown in Figure 2;

[0065] S3, converting the frame image into a deformation graph: introducing an embedded deformation graph in graph theory into the frame image, that is, the frame image S i is described as a set composed of a point set p i and an edge set χ i , n i , m i are the maximum number of points and the maximum number of edges of the frame image respectively, i is the frame number in the frame image, i∈[1, I], I is the total number of frame images, then: S i ={p i ,χ i};

[0066] S4, registration between frame images: registering each frame image divided in S2 in a non-linear mapping relationship, considering the spatial continuity and the subtle distinction degree of the apparent characteristics in the data collection process, a registration process and optimization target with higher difference tolerance degree should be considered, including the following steps:

[0067] S41, Establish the registration mapping equation: Establish a registration mapping relationship E between the frame images of the i-th frame and the j-th frame. wicp Frame j is the frame to be registered with frame i, and we have: E wicp =min(E pr +β wicp E nreg +γ wicp E lor );

[0068] Among them, E pr To align the description items; E nreg For regularization description; E lor For local transformation terms; β wicp and γ wicp The index weight, β, is determined by the degree of difference between frames. wicp and γ wicp The range of values ​​for is (0, 1), j∈[1, 1];

[0069] S42, Calculate the alignment descriptor E pr ,have:

[0070] Among them, f wel (x) is the Welsch function; Let y be a point within the frame image of the i-th frame, and y be the index of the point in the frame image of the i-th frame; y∈[1,n] i ]; yes The nearest target point within the frame image to be registered; c wel It is greater than 0 and makes f wel A constant that monotonically increases on the positive half-axis;

[0071] S43, Calculate the regularization descriptor E nreg ,have:

[0072] Where, c′ wel c′ is the updated setting constant term in the Welsch function. wel It is greater than 0 and makes f wel A constant that monotonically increases on the positive half-axis; Indicates the midpoint of the frame image to be registered. The set of neighboring points, z is the number of a point in the frame image of the j-th frame, z∈[1,n]. j ], n j The total number of points in the frame image of the j-th frame; To consider the transformation form of each point in the deformation diagram of spatial cross-coupling;

[0073] in, affine rotation matrix of the embedded morphing map; and and

[0074] S44, calculating the local transformation term E lor , where:

[0075] where η proj is a modification factor considering the local transformation intensity, and its value range is (0, 1); the operator symbol ||·||F F is the F-norm solution considering the differential local rigidity state;

[0076] S45, solving the nonlinear optimization formula: according to the registration mapping relationship E wicp of step S41, solving the best registration relationship between all the to-be-registered frame images of the highest confidence fastener point cloud, and correcting the positions of the adjacent frame images according to the relationship; obtaining the corrected point cloud data.

[0077] S5, simplifying the optimized point cloud: after the nonlinear optimization of the fastener point cloud as described in step S4, additional point cloud topological structures, point-line-edge relationships, and possible spatial outliers, data jitter, and local burrs will be introduced, which will affect the accuracy of subsequent point cloud processing, so the point cloud result processed in S4 needs to be simplified using an ellipsoid space. The spatial dimension optimization process of the point cloud data is converted into a growth process of multiple base spaces into a feature-inclusive space, and generalized elliptical parameter diffusion is performed, including the following steps:

[0078] S51, establishing an initial ellipsoid space: taking a point p tag in the corrected point cloud data in S4 as the center of the ellipsoid space; the initial values of the lengths of the major axis and the minor axis of the ellipsoid space are twice the distances between the center of the ellipsoid space and the nearest neighbor points in the offset axis direction and the depth axis direction, respectively, and the nearest neighbor point is the point in the point cloud data that has the smallest Euclidean distance from the center of the ellipsoid. The major axis direction of the ellipsoid in the ellipsoid space is parallel to the offset axis direction, and the minor axis direction is parallel to the active axis direction. The two end points of the major axis of the ellipsoid space are O au and O ad , respectively; the two end points of the minor axis of the ellipsoid space are O bu and O bd , respectively; and an initial ellipsoid space is obtained.

[0079] An ellipsoid space set is constructed, and the initial ellipsoid space is added as the first element in the set.

[0080] S52, ellipsoid space diffusion, including the following steps:

[0081] S521, randomly determine point p within the point cloud data processed by S4. k , and p k It is a point outside each ellipsoidal space in the ellipsoidal space set, for each point p k If the following condition is met, execute S522; otherwise, execute S523.

[0082] in, The normal vector of the plane formed by the major and minor axes of the ellipse and ξ dis For p k The distance threshold to the projection surface, ξ ang ξ is the threshold for vector sway. dis and ξ ang The value is determined based on the fastener to be registered, and the projection plane is p. k The center O of the ellipse in the corresponding ellipsoidal space k The plane containing the corresponding frame; p k The actual center O of the ellipse k If there are multiple ellipsoidal spaces, then a point p must be taken outside each ellipsoidal space. k Therefore, each p here k Each has a corresponding ellipse center O k .

[0083] S522, Reconstructing the ellipsoidal space:

[0084] Point p k It is integrated into its corresponding ellipsoidal space, and the center of the ellipse O k Remaining unchanged, the lengths of the major and minor axes of the corresponding ellipsoidal space are changed to point p. k The length a projected onto the major axis of the ellipse ′ el and the length b′ projected onto the minor axis el The reconstructed ellipsoidal space is obtained; where,

[0085] O kau For O k O is the endpoint of the major axis of the ellipsoid centered at a circle. kbu For O k The endpoint of the minor axis of the ellipsoid centered at the circle;

[0086] For the reconstructed ellipsoidal space, p k Located on its surface, all point cloud data within the reconstructed ellipsoidal space are changed to the ellipse's center point O. k The two endpoints of the major axis of the reconstructed ellipsoidal space, the two endpoints of the minor axis of the reconstructed ellipsoidal space, and p k.

[0087] Step S53 is executed;

[0088] S523, additional ellipsoid space is built;

[0089] According to the position of point p k , the diffusion direction of the ellipsoid space is determined;

[0090] Wherein, qua(p k ) represents the quadrant where point p k is located.

[0091] The nearest neighbor point p k of p w is determined in the obtained diffusion direction; p w is taken as the ellipsoid center of the ellipsoid space to build an additional ellipsoid space, the lengths of the major axis and the minor axis of the ellipse of the additional ellipsoid space are the lengths of the major axis and the minor axis of the ellipse of the point p k The lengths of the major axis and the minor axis of the additional ellipsoid space are projected, and all point cloud data in the additional ellipsoid space are retained;

[0092] The additional ellipsoid space is added to the ellipsoid space set, and step S53 is executed.

[0093] S53, step S52 is repeated until the ellipsoid spaces in the ellipsoid space set cover all the point cloud data after the correction in S4, and the obtained point cloud is output as the high-quality point cloud data after the nonlinear registration. The process of the reconstruction or the building of the ellipsoid space is shown in FIG. 3.

[0094] The method of the present application realizes the registration and alignment of the local details of the collected point cloud and the average of the data errors through the establishment of three types of description items under the global optimization perspective. On the one hand, the collected point cloud is modified by the multiple frame point clouds segmented from each other, which avoids the blurring of the local details and the appearance of the data holes, and improves the data accuracy of the collected point cloud; on the other hand, the error average between the multiple frame point cloud data is realized in the point cloud registration process guided by the nonlinear optimization idea, and the data confidence of the collected point cloud is improved.

[0095] In the process of nonlinear optimization, by referring to the spatiotemporal continuity of the point cloud data in the acquisition process, the acquired point cloud is regarded as a finite subdivision of a complex rigid body composed of multiple simple structure rigid bodies. Therefore, in view of the two major contradictions of local difference and global uniformity, detail rigidity distinction and overall non-rigidity, the following problems are mainly considered in the optimization process: (1) the difference in data tolerance caused by measurement error, (2) the low resolution of gradient descent caused by single optimization perspective, and (3) the weak spatial structure similarity caused by sparse point cloud. The to-be-fitted term in the nonlinear optimization objective equation is further decomposed and quantified into three types of description terms, respectively, to improve the accuracy, coverage and confidence index in the optimization process.

[0096] The algorithm establishes the deep coupling relationship between the acquired point cloud and the target point cloud through the three types of description terms designed in the optimization process, and guarantees the robustness of the algorithm with the independent optimization structure. At the same time, the algorithm retains the effective features of the acquired point cloud data as much as possible on the basis of removing noise and random points, and has strong detail retention. In addition, the iterative optimization process of the algorithm decomposes the traditional single equation gradient descent process into a three-way calculation process, and some intermediate results can be reused, further improving the calculation efficiency and avoiding falling into the local optimization trap. The above processes are all point cloud feature representation and optimization description, and have no additional requirements and restrictions on the basic attributes and data distribution of the point cloud, so they have strong universality and can be further applied to non-rigid target registration scenarios, such as human body reconstruction, high-resolution reconstruction, high-detail virtual reality, etc.

Claims

A method of nonlinear registration optimization of a fastener point cloud, characterized in that, The method comprises the following steps: S1, original data acquisition: using a collection device to collect point cloud data containing spatial coordinates and timestamp information in the active axial, depth axial and offset axial; the active axial is the curve tangent direction of the movement of the collection device, and the active axial, the depth axial and the offset axial are perpendicular to each other; S2, data preprocessing: dividing the point cloud data collected in S1 into several frame images according to the timestamp information in the form of spatial slices according to the time axis; S3, converting the frame images into deformation maps: S i = {p i ,χ i}; wherein S i is a frame image of the i-th frame, composed of a point set p i and an edge set χ i ; n i is the total number of points in the frame image of the i-th frame, m i is the total number of edges in the frame image of the i-th frame, i is the number of the frame image, i∈[1, I], and I is the total number of frame images. S4, registration between frame images: First, the registration mapping relationship E between frame images is constructed wicp There are: E wicp = min(E pr + β wicp E nreg + γ wicp E lor ); wherein: E pr is an alignment description term; E nreg is a regularization description term; E lor is a local transformation term; β wicp and γ wicp are index weights, the values of said β wicp and said γ wicp depending on the degree of differentiation between the frame images, said β wicp and γ wicp ranging between (0, 1), Then, according to the registration mapping relationship E wicp An optimal registration relationship is obtained, and all frame images obtained in S2 are registered according to the optimal registration relationship, to obtain corrected point cloud data. S5, simplifying the corrected point cloud data, comprising the following steps: S51, establishing an initial ellipsoid space: First, a point p in the modified point cloud data obtained by S4 tag an ellipsoid space; a length of an ellipsoid long axis of the ellipsoid space is initially set as twice a distance between the ellipsoid center and a nearest neighbor point projected in the offset axis direction, and a length of an ellipsoid short axis of the ellipsoid space is initially set as twice a distance between the ellipsoid center and the nearest neighbor point projected in the depth axis direction; Wherein, the nearest neighbor point is the point in the corrected point cloud data with the minimum Euclidean distance from the center of the ellipse; Then, the direction of the long axis of the ellipse is parallel to the shift axis, and the direction of the short axis is parallel to the depth axis, and the two endpoints of the long axis of the ellipse are O au and O ad ; the two endpoints of the short axis of the ellipse are O bu and O bd ; to obtain an initial ellipsoid space; Finally, an ellipsoid space set is constructed, and the initial ellipsoid space is added as the first element in the ellipsoid space set; S52, ellipsoid space diffusion: adding or reconstructing the ellipsoid space in the ellipsoid space set, and simplifying the S4 corrected point cloud data covered by the ellipsoid space; S53, repeating step S52 until the ellipsoid space in the ellipsoid space set covers all the S4 corrected point cloud data, and outputting the obtained point cloud as high-quality point cloud data after nonlinear registration. The method of nonlinear registration optimization of a point cloud of fasteners of claim 1, wherein: The alignment description item E in S4 pr This is calculated by the following method: wherein f wel (x) is the Welsch function; for a point within the frame image of the ith frame, y is the number of the point in the frame image of the ith frame; y e [1, n] i ]; ​ is the nearest target point in the frame image to be registered, setting the frame to be registered as the jth frame, j ∈ [1, I]; c wel is greater than 0 and such that the f wel is a constant that is monotonically increasing on the positive half-axis. The method of nonlinear registration optimization of a point cloud of fasteners according to claim 2, characterized in that: The regularization description term E in S4 nreg The following method is used for calculation: where c' is a constant term in the Welsch function updated as wel c' = c' + 0.1 wel is a constant greater than 0 and such that the f wel is monotonically increasing on the positive half-axis; representing a point in the frame image to be registered a set of neighboring points of the point z, z is a point number in the frame image of the jth frame, z e [1, n j ], n j is a total number of points in the frame image of the jth frame; The transformation form of each point in the deformation map is considered for spatial cross coupling. The nonlinear registration optimization method for fastener point cloud according to claim 3, characterized in that: The regularization description term The following methods were used to calculate: wherein affine rotation matrix for an embedded morphed image; and The affine translation vector of the embedded deformation map corresponding to the i-th frame and the j-th frame, respectively. The method of nonlinear registration optimization of a point cloud of fasteners of claim 4, wherein: The local transformation term E lor The local transformation term E is calculated by the following method: where η proj is a modification factor considering the local transformation severity, which takes the value range (0, 1); the operator symbol ||·||F F is the F-norm solution considering the differential local rigid state. The method of nonlinear registration optimization of a point cloud of fasteners of claim 1, wherein: S52 specifically comprises the following steps: S521, for each ellipsoid space in the ellipsoid space set, randomly determining a point p outside the ellipsoid space k , the p k is a point in the modified point cloud data obtained in S4, for each point p k , when it satisfies the following formula, S522 is performed, otherwise S523 is performed, wherein, a normal vector to a plane formed by the major axis and the minor axis of the ellipse and ξ dis for a point p k distance threshold to the projection plane, ξ ang for a vector yaw threshold; the ξ dis and ξ ang values are determined according to the fastener to be registered, the projection plane being p k the corresponding ellipsoid space's ellipsoid center O k the plane in which the corresponding frame lies; S522, reconstructing the ellipsoid space: The point p k is merged into its corresponding ellipsoidal space, and the ellipsoidal space has an ellipsoidal center O k unchanged, the length of the long axis of the corresponding ellipsoidal space is changed to a' el , and the length of the short axis of the ellipsoid is changed to b' el , to obtain the reconstructed ellipsoidal space, and has: wherein O kau is the long axis endpoint of the ellipsoidal space with O k as the center, O kbu is the short axis endpoint of the ellipsoidal space with O k as the center; Change all the point cloud data covered in the reconstructed ellipsoid space to the ellipsoid center point O k , the two end points of the long axis of the reconstructed ellipsoid space, the two end points of the short axis of the reconstructed ellipsoid space and the p k ; Step S53 is executed; S523, adding an ellipsoid space: First, the position of the point p k with the center O of the ellipse k of the ellipsoid space: Then, a nearest neighbor point p k of the point p w is determined in the diffusion direction; the point p w is added as an ellipsoid space of an ellipsoid center to build an additional ellipsoid space, an ellipsoid major axis of the additional ellipsoid space has a length of a length of the point p k projected on an ellipsoid major axis of the additional ellipsoid space; and an ellipsoid minor axis of the additional ellipsoid space has a length of a length of the point p k projected on an ellipsoid minor axis of the additional ellipsoid space, all point cloud data in the additional ellipsoid space is reserved. The additional ellipsoid space is added to the ellipsoid space set, and step S53 is executed. The method of nonlinear registration optimization of a point cloud of fasteners of claim 6, wherein: The diffusion direction described in S523 is determined by the following equation: wherein qua(p k ) denotes the quadrant in which point p k lies. The method of nonlinear registration optimization of a point cloud of fasteners of claim 1, wherein: The collection device in S1 is a self-moving rail transit mobile three-dimensional scanning system.

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