A large-scale bridge three-dimensional laser scanning planning method under a complex scene

Abstract

The present application relates to a kind of large-scale bridge three-dimensional laser scanning planning method under complex scene, comprising:1) bridge simulation scene construction;2) station planning;3) target planning;4) scanning scheme output.The present application proposes the optimization method for minimizing the number of target required for point cloud registration, which alleviates the problem of low efficiency caused by scanning scheme based on target registration;At the same time, considering the scanning azimuth angle condition of high scanning resolution station, it can meet the detail level and completeness requirements of the region of interest while reducing the time cost;It provides a general solution for the optimization arrangement of station and target for complex bridge scanning scene, which can be applied to three-dimensional laser scanning planning problems in other bridge scenes.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of bridge detection, and particularly relates to a large-scale bridge three-dimensional laser scanning planning method under a complex scene. BACKGROUND

[0002] With the development of modern bridges towards large-span, high-pier, high-tower and giant segment factory prefabrication, higher and higher requirements are put forward for bridge geometric shape measurement. The representative three-dimensional laser scanner in advanced measurement equipment can quickly obtain geometric shape related data of the measured object with millimeter level precision, providing a new technical means for accurate bridge geometric state detection. In actual projects, the data acquisition work of the three-dimensional laser scanner usually depends on the subjective operation of the inspector, resulting in uncontrollable data quality. Although some scholars have proposed scanning planning methods for bridge scenes, these methods are usually for bridges with relatively simple geometric shapes, such as beam bridges. The scanning planning problem of large-scale bridges under complex scenes has not been fully solved. There are two main differences between the scanning planning of large-scale bridges under complex scenes and the scanning planning of simple geometric shape bridges.

[0003] Firstly, the positions available for erecting the three-dimensional laser scanner under complex scenes are limited, and are usually erected on the bridge deck to scan high-altitude components or under the bridge to scan the overall bridge shape. In order to meet the detail level and completeness requirements of the region of interest, a long-distance and high scanning resolution scanning mode must be used, and the use of panoramic scanning in the high scanning resolution mode will inevitably result in greater time cost. When planning the scanning, the scanning azimuth angle of the high scanning resolution station is considered to balance the scanning efficiency and data quality, which meets the actual needs.

[0004] Secondly, the subsequent point cloud registration conditions need to be considered when formulating the scheme. The general simplified registration condition method requires that the overlap rate of two registration point clouds be greater than 20%, but the inherent noise and outliers of point cloud data, as well as incomplete scanning, strictly require the accuracy of the matching feature extraction algorithm, especially since there is a lack of mature point cloud processing algorithm to realize accurate registration under complex scenes. Another method is to introduce target-based point cloud registration, which is still a relatively common way. However, the existing research lacks exploration of the scanning planning problem of the target-based point cloud registration method, and the main reason is the potential low efficiency. When formulating the scheme, the number of scanning targets is reduced as much as possible from the global perspective of the station and target distribution to improve the cost-effectiveness.

[0005] Therefore, it is urgently needed to propose a large-scale bridge three-dimensional laser scanning planning method under a complex scene to formulate a scanning scheme and guide the field scanning. SUMMARY

[0006] The purpose of the present application is to overcome the deficiencies of the prior art, provide a large-scale bridge three-dimensional laser scanning planning method under complex scene, use the surface point cloud extracted from the bridge three-dimensional model as the simulation scene, consider the multiple constraint conditions of the user's data requirements and the used scanner parameters, and design based on the weighted greedy algorithm, the ant colony algorithm and the density clustering algorithm.

[0007] The present application solves its technical problems by the following technical solutions:

[0008] A large-scale bridge three-dimensional laser scanning planning method under complex scene, the steps of the method are:

[0009] S1, using unmanned aerial photogrammetry to reconstruct a bridge three-dimensional model, adding scanning obstacles in the bridge three-dimensional model, extracting the center point and center point normal vector of the model triangle, and constructing a bridge entity point cloud, segmenting the area capable of erecting a scanner and placing a target in the entity point cloud and discretizing it into equidistant grid points as a station and target candidate point set;

[0010] S2, under unconstrained conditions, virtually pre-scanning the entity point cloud to obtain a surface point cloud, down-sampling and segmenting a special area as a bridge simulation scene;

[0011] S3, according to user requirements and scanner parameters, setting the constraint conditions of global area point cloud density, special area point cloud density, maximum incident angle, maximum scanning distance and scanning angle resolution range, carrying out ray tracing simulation analysis on the bridge simulation scene under the constraint conditions, optimizing the two-stage station layout through the weighted greedy algorithm, selecting the optimal station position, scanning azimuth angle of the high scanning angle resolution station, and according to the station layout optimization result, taking the shortest scanning path of the scanner and the maximum number of overlapping points of adjacent stations as the target, carrying out path planning on the optimal station through the ant colony algorithm;

[0012] S4, according to the station path planning result, clustering adjacent stations into a station group, based on the target candidate point set and the distribution of the station group, taking the minimum target coordinate covariance condition number as the principle, optimizing the target layout, and finding the optimal target position of each group of stations;

[0013] S5, summarizing the station and target layout optimization results to output a scanning scheme, which includes the optimal station position and station group, the optimal target position of each group of stations, the scanning azimuth angle of the high scanning angle resolution station, and the scanning path.

[0014] Moreover, the specific process of the virtual pre-scanning in S2 is as follows:

[0015] First, according to the scanning planning experience of measurement professionals, a group of virtual pre-scanning station positions are selected;

[0016] Secondly, based on the virtual pre-scan station position, the ray tracing simulation analysis is carried out on the entity point cloud under the condition of no constraint, and all visible points are saved as the bridge surface point cloud.

[0017] Moreover, the two-stage station arrangement optimization process in S3 is as follows:

[0018] Firstly, the panoramic scanning mode with low scanning angle resolution is adopted to count the region position in the scene that meets the constraint condition, and to judge whether the special region meets the constraint condition;

[0019] Secondly, if the constraint condition is not met, the scanning mode with high scanning angle resolution and limited azimuth is used for simulation in the special region first, and then the panoramic scanning mode with low scanning resolution is simulated;

[0020] Thirdly, according to the number of scene points meeting the constraint condition, the weight coefficient of each station candidate point is calculated, and the station with the maximum weight is selected iteratively until the coverage rate difference between adjacent two iterations is less than the specified threshold, and the iteration is stopped, and a set of optimal station positions and scanning angle resolutions are obtained.

[0021] Moreover, the path planning process in S3 is as follows:

[0022] Firstly, the ant colony algorithm is adopted, a plurality of ants are distributed to each station, the transfer probability of each ant to the next station is calculated according to the distance between the stations and the scanning overlap rate, the next station is selected by the roulette method, and the order of the stations passed by the ants and the path length are recorded until all the stations are visited, and the elite ants are selected in ascending order according to the total length of the path of each ant;

[0023] The above steps are repeated until the preset iteration number is met, and the order of the stations passed by the elite ants and the path length are the global path planning.

[0024] Moreover, the station grouping process in S4 is as follows:

[0025] Firstly, the median of the distance between all two adjacent stations is counted, a neighborhood circle is drawn with the median as the radius and a certain station as the center, neighborhood detection is carried out, if the number of stations in the neighborhood is equal to or exceeds the number threshold, the station is a core station, otherwise it is a noise station. The number threshold is set to 2;

[0026] Secondly, all stations in the neighborhood are detected until noise stations are detected, all detected core stations are regarded as the same group of stations, and noise stations are regarded as a separate group of stations;

[0027] Finally, the above steps are repeated for the remaining stations until all stations are divided into a plurality of station groups.

[0028] Further, the target arrangement optimization in S4 is specifically as follows:

[0029] First, for the same station group, 3 target arrangement points are selected from the visible target candidate points according to the principle of minimum target mutual visibility and coordinate covariance matrix condition number in the group;

[0030] Second, for the adjacent station group, two nearest stations are determined according to the path planning result, 3 target positions are selected from the visible target candidate points according to the principle of minimum target mutual visibility and coordinate covariance matrix condition number, and the above steps are repeated until all station groups are traversed.

[0031] Further, the maximum incident angle is not greater than 70 degrees.

[0032] The advantages and beneficial effects of the present application are:

[0033] 1. The present application proposes a scanning planning method for the bridge-on-bridge scene, which provides guidance for formulating a typical bridge scene scanning scheme;

[0034] 2. The present application considers the scanning azimuth angle condition of high scanning resolution stations, which can meet the detail and completeness requirements of the region of interest while reducing the time cost;

[0035] 3. The present application proposes an optimization method for minimizing the number of target points required for point cloud registration, which alleviates the low efficiency problem caused by the scanning scheme based on target registration;

[0036] 4. The scanning planning method proposed by the present application provides a general solution for the optimal arrangement of stations and targets in complex bridge scanning scenes, and can be applied to three-dimensional laser scanning planning problems in other bridge scenes. BRIEF DESCRIPTION OF DRAWINGS

[0037] Figure 1 The flowchart of the present application;

[0038] Figure 2 The three-dimensional model processing process diagram of the present application;

[0039] Figure 3 The station and target candidate point set establishment process diagram of the present application;

[0040] Figure 4 The simulation scene establishment process diagram of the present application;

[0041] Figure 5 The scanning azimuth diagram of the present application;

[0042] Figure 6 The target arrangement optimization diagram of the present application;

[0043] Figure 7 This is a diagram of the scanning planning scheme for the present invention;

[0044] Figure 8 This is a scanned image of the bridge as described in this invention. Detailed Implementation

[0045] The present invention will be further described in detail below through specific embodiments. The following embodiments are merely descriptive and not limiting, and should not be used to limit the scope of protection of the present invention.

[0046] like Figure 1 As shown, a method for planning large-scale bridge 3D laser scanning in complex scenarios is innovative in that the method includes the following steps:

[0047] Step 1: For existing 3D bridge models or 3D bridge models reconstructed using UAV photogrammetry, use CloudCompare software to add scanned obstacles to the bridge surface, extract the center points and normal vectors of the model triangles, and construct the bridge entity point cloud accordingly. Figure 2 As shown, the area in the segmented point cloud where a scanner and target can be placed is discretized into equidistant grid points, serving as a candidate point set for the station and target, as follows. Figure 3 As shown;

[0048] In step 1, the construction of the candidate point set for the measuring station and the target is carried out as follows:

[0049] The bridge surface region of the entity point cloud is segmented and discretized into a sparse point cloud at 1m intervals, serving as the candidate point set for bridge monitoring stations. The occluded top surface region of the 3D model is segmented and discretized into a sparse point cloud at 1m intervals, then fused with the candidate point set for bridge monitoring stations to serve as the candidate point set for targets on the bridge. The location of the bridge is searched using Google Maps, and the bridge and its surrounding scene area are captured from the map. The scene image is converted into a grayscale image, and the white edge contours are discretized into a sparse point cloud at 1m intervals, serving as the candidate point set for monitoring stations under the bridge.

[0050] Step 2: Based on user requirements and instrument parameters, set constraints for global point cloud density, specific area point cloud density, maximum incident angle, maximum scanning distance, and scanning angular resolution range. Under unconstrained conditions, perform a virtual pre-scan of the entity point cloud to obtain the surface point cloud. Downsample and segment specific areas to create a bridge simulation scene. The specific process of this step is as follows: Figure 4 As shown;

[0051] In step 2, the maximum incident angle of the constraint condition is set to be not greater than 70 degrees, the global LOD requirement is set to be 0.025 m, special LOD requirements of 0.008 m and 0.005 m are set for the arch end and the main beam notch section, the scanning distance on the bridge is 120 m, and the scanning distance under the bridge is 270 m. According to the user interface of the scanner Leica P50, the maximum scanning distance of 120 m corresponds to the optional 0.005 degrees, 0.009 degrees, 0.018 degrees, and 0.036 degrees, and the maximum scanning distance of 270 m corresponds to the optional 0.005 degrees, 0.009 degrees, 0.018 degrees, and 0.036 degrees.

[0052] In step 2, the virtual pre-scanning step is as follows:

[0053] First, according to the scanning planning experience of the measurement professional, a group of virtual pre-scanning station positions are selected;

[0054] Secondly, based on the virtual pre-scanning station positions, ray tracing simulation analysis is carried out on the entity point cloud without constraint conditions, and all visible points are saved as bridge surface point cloud.

[0055] Step 3: Under the constraint condition, ray tracing simulation analysis is carried out on the bridge simulation scene, two-stage station layout optimization is carried out through weighted greedy algorithm, and a group of optimal station positions and scanning angle resolution are selected, as shown in the following table: Figure 5

[0056] In step 3, the specific process of two-stage station layout optimization is as follows:

[0057] First, the panoramic scanning mode with low scanning angle resolution (0.036 degrees, 0.018 degrees, 0.009 degrees) is used to count the region positions in the scene that meet the constraint conditions, and to judge whether the special region meets the constraint conditions;

[0058] Secondly, if the constraint condition is not met, the scanning mode with high scanning angle resolution (0.005 degrees) is used to simulate the special region first, and then the panoramic scanning mode with low scanning resolution is used to simulate. The scanning angle resolution mentioned above, the smaller the angle, the higher the resolution.

[0059] Thirdly, according to the number of scene points meeting the constraint condition, the weight coefficient of each station candidate point is calculated, the station with the maximum weight is iteratively selected, until the completeness requirement of all scanning points is met, or the difference between the coverage rates of two adjacent iterations is less than 2%, then the iteration is stopped, and a group of optimal station positions and scanning angle resolution are obtained.

[0060] In step 3, the method for calculating the weight coefficient is as follows:​

[0061] First, according to the visibility analysis, determine the candidate scanning site S i The visibility index V j of the observation point T ij :

[0062] V ij ={0,1}

[0063] Where: V ij =0, indicating that the scanning object T j can be seen by the candidate scanning site S i ; V ij =1, indicating that the scanning object T j cannot be seen by the candidate scanning site S i ; S i is any element in the candidate scanning site set {S}; T j is any element in the candidate scanning object set {T}.

[0064] Second, according to the visibility index V ij , calculate the weight coefficient n i of each observation point, the calculation formula is as follows:

[0065]

[0066] Third, according to the visibility index V ij , calculate the weighted weight coefficient w i of each observation point, the calculation formula

[0067] is as follows:

[0068]

[0069] Finally, according to the weight coefficient n i and w i of the observation point, calculate the normalized weight coefficient W i of each candidate scanning site, the calculation formula is as follows:

[0070]

[0071] In step 3, the specific process of path planning is as follows:

[0072] First, assign several ants to each scanning station, calculate the transition probability of each ant to the next scanning station according to the distance between the scanning stations and the scanning overlap rate, select the next scanning station by roulette method, record the order of the scanning stations passed by the ants and the path length, until all scanning stations are visited, select the elite ant by ascending order of the total length of each ant path;

[0073] Repeat the above steps until the preset number of iterations is met, at which time the order of the stations walked by the elite ants and the path length are the global path planning.

[0074] Step 4: According to the station path planning result, cluster the adjacent stations into a station group, and based on the distribution of the target candidate point set and the station group, optimize the target arrangement according to the principle of minimum target mutual visibility and target coordinate covariance condition number, and find the optimal target position of each station group, as shown in Figure 6 .

[0075] In the step 4, the specific process of station grouping is as follows:

[0076] First, the median of the distance between all two adjacent stations is calculated, and a neighborhood circle is drawn with the median as the radius and a certain station as the center. If the number of stations in the neighborhood is equal to or exceeds the number threshold, the station is a core station, otherwise it is a noise station. The number threshold is set to 2;

[0077] Second, all stations in the neighborhood are detected until noise stations are detected. All detected core stations are considered as the same group of stations, and noise stations are a separate station group

[0078] Finally, repeat the above steps for the remaining stations until all stations are divided into multiple station groups.

[0079] In the step 4, the specific process of target arrangement optimization is as follows:

[0080] First, for the same group of stations, select three target arrangement points from the visible target candidate points according to the principle of minimum target mutual visibility and coordinate covariance matrix condition number;

[0081] Second, for adjacent station groups, according to the path planning result, determine two adjacent stations, and select three target arrangement points from the visible target candidate points according to the principle of minimum target mutual visibility and coordinate covariance matrix condition number. Repeat the above steps until all station groups are traversed.

[0082] In the step 4, the formula of the covariance matrix condition number is as follows:

[0083] k(S t )=Cand(Cov(S t )) / S St ,(S t ∈Ω)

[0084] Where S t is the coordinate matrix of the three selected target spheres, Cov represents the covariance of the matrix, Cand represents the condition number of the matrix, and S StΩ represents the area surrounded by three selected targets, and Ω is the intersection of the visible areas.

[0085] Step 5: The station and target arrangement optimization results are summarized and the scanning scheme is visualized and output, as shown in Figure 7 The scanning scheme is used to guide the field scanning.

[0086] In the figure, the stations are red triangles, the station group number and scanning resolution are marked beside the stations, there are a total of five station groups, and the stations are numbered from 0 to 5, the scanning resolution is represented by 1 / 1 (angular resolution is 0.005°), 1 / 2 (angular resolution is 0.009°), and 1 / 3 (angular resolution is 0.018°), the same group of targets are represented by the same color, the station group number that can be used for splicing the stations is marked, for example, target 0-1 represents that the station group 0 and the station group 1 scan this group of targets, and the green straight line represents the scanning path. The P4S scheme has a total of 20 stations, including 16 stations on the bridge and 4 stations under the bridge.

[0087] When scanning on the bridge, the time for moving the station, setting the scanning parameters, and scanning the targets is about 5 minutes, the time for placing the targets is about 3 minutes, there are 16 stations, 9 groups of targets, and the scanning time is 270 minutes. When scanning under the bridge, the time for moving the station and setting the scanning parameters is about 10 minutes, there are 4 stations, and the scanning time is 148 minutes. Therefore, the total scanning time is 418 minutes.

[0088] The scheme sets a scanning angle of 15 degrees for two high scanning resolution stations (scanning resolution is 1 / 1) on the bridge, and the number of targets required for registering the stations on the bridge is reduced from 15 groups (45 targets) to 9 groups (27 targets).

[0089] Compared with the scheme without setting the scanning angle and simplifying the number of targets, the time for placing and scanning a group of targets is calculated as 6 minutes, the total scanning time of the scheme is shortened from 554 minutes to 418 minutes, and the reduction rate is about 25%.

[0090] Although the embodiments of the present application and the drawings are disclosed for illustrative purposes, those skilled in the art can understand that various alternatives, changes and modifications are possible without departing from the spirit and scope of the present application and the appended claims, therefore, the scope of the present application is not limited to the disclosed contents of the embodiments and the drawings.

Claims

1. A method for planning large-scale bridge 3D laser scanning in complex scenarios, characterized in that: The steps of the method are: S1, using unmanned aerial photography surveying to reconstruct a bridge three-dimensional model, adding scanning obstacles in the bridge three-dimensional model, extracting the center points and normal vectors of the model triangles, and constructing a bridge entity point cloud, segmenting the area capable of erecting a scanner and placing a target in the entity point cloud and discretizing it into equidistant grid points as a candidate point set of stations and targets; S2, under unconstrained conditions, performing virtual pre-scanning on the entity point cloud to obtain a surface point cloud, down-sampling and segmenting a special area as a bridge simulation scene; S3, according to user requirements and scanner parameters, setting constraint conditions of global area point cloud density, special area point cloud density, maximum incident angle, maximum scanning distance and scanning angle resolution range, performing ray tracing simulation analysis on the bridge simulation scene under the constraint conditions, performing two-stage station layout optimization through a weighted greedy algorithm, selecting optimal station positions, scanning angle resolutions and scanning azimuth angles of high scanning angle resolution stations, and according to the station layout optimization results, taking the shortest scanning path of the scanner and the maximum number of scanning overlapping points of adjacent stations as targets, performing path planning on the optimal stations through an ant colony algorithm; S4, according to the station path planning results, clustering adjacent stations into station groups, and based on the distribution of the candidate point set of targets and the station groups, performing target layout optimization according to the principle of minimum target coordinate covariance condition number and target mutual visibility, and finding the optimal target positions of each station group; S5, outputting a scanning scheme by summarizing the station and target layout optimization results, wherein the scanning scheme includes optimal station positions and station groups, optimal target positions of each station group, scanning angle resolutions, scanning azimuth angles of high scanning angle resolution stations and scanning paths; The path planning process in S3 is as follows: Firstly, the ant colony algorithm is used to assign a plurality of ants to each station, a transition probability of each ant to the next station is calculated according to the distance between stations and the scanning overlap rate, the next station is selected through a roulette method, the order of stations passed by the ants and the path length are recorded, until all stations are visited, and elite ants are selected by ascending order of the total length of each ant path; The above steps are repeated until a preset iteration number is met, and the order of stations passed by the elite ants and the path length are the global path planning; The station grouping process in S4 is as follows: Firstly, the median of the distance between all adjacent stations is calculated, a neighborhood circle is drawn with the median as the radius and a certain station as the center, neighborhood detection is performed, if the number of stations in the neighborhood is equal to or greater than a threshold value, the station is a core station, otherwise it is a noise station, wherein the threshold value is 2; Secondly, neighborhood detection is performed on all stations in the neighborhood until noise stations are detected, all detected core stations are regarded as the same station group, and noise stations are regarded as separate station groups; Finally, the above steps are repeated for the remaining stations until all stations are divided into a plurality of station groups; The target layout optimization process in S4 is as follows: Firstly, for the same station group, three target layout points are selected from the visible target candidate points according to the principle of minimum target coordinate covariance matrix condition number and target mutual visibility; Secondly, for the adjacent station group, according to the path planning result, two nearest stations are determined, and three target positions are selected in the visible target candidate points according to the principle of minimum target mutual visibility and coordinate covariance matrix condition number of two adjacent stations, and the above steps are repeated until all station groups are traversed.

2. The method of claim 1, wherein the method is characterized by: The specific process of the virtual pre-scanning in S2 is as follows: Firstly, a group of virtual pre-scanning station positions are selected according to the scanning planning experience of measurement professionals; Secondly, based on the virtual pre-scanning station positions, ray tracing simulation analysis is performed on the solid point cloud under the condition of no constraints, and all visible points are saved as bridge surface point cloud.

3. The method of claim 1, wherein the method is characterized by: The specific process of the two-stage station layout optimization in S3 is as follows: Firstly, the panoramic scanning mode with low scanning angle resolution is used to count the region positions in the scene that meet the constraint condition, and to judge whether the special region meets the constraint condition; Secondly, if the constraint condition is not met, the scanning mode with high scanning angle resolution and limited azimuth is used for simulation for the special region first, and then the panoramic scanning mode with low scanning resolution is used for simulation; Thirdly, according to the number of scene points meeting the constraint condition, the weight coefficient of each station candidate point is calculated, the station with the maximum weight is selected iteratively until all scanning points meet the completeness requirement, or the difference between the coverage rates of two adjacent iterations is less than a specified threshold, then the iteration is stopped, and a group of optimal station positions and scanning angle resolutions are obtained.

4. The method of claim 1, wherein the method is characterized by: The maximum incident angle is not greater than 70 degrees.

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