Ineffective fixed solution cave three-dimensional laser point cloud space registration method

Abstract

The invention discloses a cave three-dimensional laser point cloud space registration method of an invalid fixed solution. The method comprises the following steps: extracting longitude, latitude and elevation data in a *. Csv format file to form an Excel file; the method comprises the following steps: directly endowing file data with a geographic coordinate system, converting to generate a vector file with the geographic coordinate system, converting the vector file into a vector file of a projection coordinate system, calculating and assigning projection coordinates of a track of scanning equipment, retaining the projection coordinates and elevation fields, selecting corresponding X, Y and Z fields, and reading. Generating three-dimensional space linear track data with projection coordinates; the three-dimensional space linear track and the three-dimensional laser point cloud data of the station are subjected to manual registration by taking the three-dimensional space linear track as a reference, and the subsequent point cloud splicing is subjected to manual registration by taking the data of the station after space registration as a reference and adopting the ground feature features of the coincident section. According to the method, effective utilization of invalid fixed solutions can be realized, and the cave measurement and splicing precision is improved.

Description

Technical Field

[0001] The invention belongs to the technical field of three-dimensional laser point cloud space registration, and relates to a cave three-dimensional laser point cloud space registration method with an invalid fixed solution. Background Art

[0002] The domestic Qiyu Innovation Lingguang L2 handheld 3D laser scanner is equipped with an external RTK. It can connect to the CORS signal in an open area to collect the coordinates of the motion trajectory, and then assign the three-dimensional spatial geographic coordinates of the site cloud (including the latitude and longitude of each point and the altitude) after solving it through the professional software LixelStudio. In the process of cave 3D mapping, the spatial geographic coordinates are assigned to the cloud data of the first site outside the cave, and the subsequent point cloud data in the cave is aligned station by station based on this (similar to the topological relationship), so that the point cloud data of the entire cave can have geographic coordinates. If the laser point cloud cannot be effectively assigned geographic spatial coordinates, the point cloud data will lose at least half of its value, and it will not be able to effectively grasp the accurate spatial position of the cave and the spatial structural relationship with the surface, and it will be impossible to carry out the necessary spatial and morphological analysis, which is very unfavorable for the cognition of the development and evolution process of the cave, hydrogeological analysis, scientific utilization, protective development and other work. Therefore, it is a crucial link to assign the spatial geographic coordinates to the first site cloud as a whole and use it as a base station to align the entire cave geographic space, which will be of great benefit to the subsequent research and application of underground space.

[0003] When using the L2 3D laser scanner in the field with an external RTK to collect data, the upper right corner of the LixelGO mobile application that controls the L2 operation will display four modes: Fixed solution (Fixed), Floating solution (Float), Single solution (Single), and Invalid. At the same time, the external RTK signal light will display three situations: green, blue, and red. The corresponding displays are usually: a. Fixed solution (green light); b. Floating solution (blue light); c. Single solution (blue light); d. Invalid (red light).

[0004] In general, if there are more than 25 satellites (green light) in an open area, the collected point cloud data can generally be solved normally (fixed solution is valid), and the three-dimensional spatial geographic coordinates of the first station cloud can be directly assigned. However, there are also about 20% of invalid fixed solutions, that is, although it is displayed as a fixed solution (green light), it cannot be successfully solved by the software due to the small number of satellites collected or poor quality. The floating solution (blue light) and single point solution (blue light) are both due to the small number of searched satellites, and the invalid and unusable (red light) is due to the small number of searched satellites or even no satellites can be found. The above three situations can be regarded as invalid on the spot, and it is necessary to extend to a more open area to collect the first station cloud data. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a cave three-dimensional laser point cloud spatial registration method for an invalid fixed solution, which can achieve effective utilization of the invalid fixed solution and improve the cave measurement and splicing accuracy.

[0006] The scheme implemented by the present invention is: A cave 3D laser point cloud spatial registration method for an invalid fixed solution. The method is as follows: after collecting the point cloud data and processing it in the office, if the fixed solution is found to be invalid, it is necessary to manually register the track coordinates (CORS signals) collected by the RTK of this station with the point cloud data, including the following steps: (1) In the first station scanning data outside the cave, project_data\algorithm\gnss.csv can be found in the folder named by date + time. This csv format file contains the trajectory coordinates, elevation values ​​and geographic coordinate system information of the scanning equipment. This information is the basis for subsequent spatial registration; (2) Convert gnss.csv to 97-2003 Excel format file, extract the three columns of longitude, latitude and elevation data in the list to form an Excel file to be read; (3) Use Add XY Data in ArcGIS software to load the Excel file, read the longitude, latitude, and elevation values ​​and directly assign them to the GCS_China_Geodetic_Coordinate_System_2000 geographic coordinate system, and convert them to generate a vector file with a geographic coordinate system; next, you need to convert from the geographic coordinate system to the projection coordinate system. Determine the projection coordinate system according to the longitude and latitude of the acquisition area, and then use the projection transformation tool to convert the vector file with the geographic coordinate system into a projection coordinate system vector file (select the corresponding belt number according to the longitude value); finally, open the attribute table in the vector file display window to add fields, and perform geometric calculations (Calculate Geometry) on the projection coordinates of the XX and YY fields in the scanning device trajectory. The Z elevation field remains unchanged, and select dBASE Table to generate a *.dbf format file; (4) For the *.dbf file in step (3), open it with Excel and edit it, retaining only the projection coordinates and elevation fields, and then export it as a *.csv file; (5) Use CloudCompare software to load the *.csv file in step (4), select the corresponding X, Y, and Z fields, and read them to generate three-dimensional linear trajectory data with projection coordinates, and output them as *.las / laz format files; (6) Use Trimble RealWorks software to read the 3D spatial linear trajectory and the first station 3D laser point cloud data in step (5) at the same time, and use the 3D spatial linear trajectory as a reference to manually align the first station 3D laser point cloud data. Finding the starting point, end point and main feature point positions is the key to improving the accuracy of manual alignment. That is, visually identify at least 3 points of overlap between the first station laser point cloud feature objects and the 3D trajectory in space. Based on this, manually fine-tune the overall matching degree between the first station 3D laser point cloud and the spatial trajectory from different angles, taking into account both horizontal and vertical accuracy to match the entire 3D spatial data. The inspection accuracy is controlled at 0.9-1.1 meters. (7) The subsequent point cloud splicing is based on the point cloud data after the spatial registration of the first station in step (6), and semi-automatic registration is performed using the three-dimensional ground feature of the overlapping segment. After manually registering two segments of three-dimensional laser point cloud data with overlapping ground feature, after achieving almost overlapping effect in the horizontal and vertical directions, the point cloud of the overlapping segment of the two stations is optimized and calculated, i.e. semi-automatic registration, which can effectively improve the registration accuracy, and the inspection accuracy is controlled at 0.3-0.4 meters.

[0007] The effects of the present invention are as follows: the present invention uses linear three-dimensional trajectories to align three-dimensional point cloud data, which can effectively make up for the three-dimensional point cloud space alignment when the fixed solution of the first station cloud is invalid. The accuracy of the first station cloud can also be controlled within 0.9-1.1 meters through manual alignment, which can meet most work requirements. The data can be effectively utilized and the effective coverage of the data is more conducive to the detection and splicing accuracy of the cave. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 A flowchart of a cave 3D laser point cloud spatial registration method with an invalid fixed solution; Figure 2 It is a schematic diagram of projection transformation and geometric calculation of an invalid fixed solution linear three-dimensional trajectory; Figure 3 It is a schematic diagram of projection transformation and coordinate assignment of an invalid fixed solution linear three-dimensional trajectory; Figure 4 A schematic diagram of an invalid fixed solution linear three-dimensional trajectory data reading option; Figure 5 A schematic diagram of successful reading of the coordinates of an invalid fixed solution linear three-dimensional trajectory data; Figure 6 It is a schematic diagram of the spatial structure of an invalid fixed solution linear three-dimensional trajectory data. DETAILED DESCRIPTION

[0009] The present invention is further described below in conjunction with specific embodiments.

[0010] Example 1: Figure 1-Figure 6As shown in the figure, a cave 3D laser point cloud spatial registration method with invalid fixed solution is provided. The method is as follows: RTK is used outside the cave to cooperate with the SLAM handheld 3D laser scanner. At the beginning of scanning, the coordinate system is set to CGCS2000 according to the received CORS signal. After synchronously collecting the point cloud data and processing it in the office, if the spatial coordinate fixed solution is found to be invalid, it is necessary to manually align the trajectory coordinates (CORS signal) collected by the RTK of this station with the point cloud data, including the following steps: (1) In the first station scanning data outside the cave, project_data\algorithm\gnss.csv can be found in the folder named by date + time. This csv format file contains the trajectory coordinates, elevation values ​​and geographic coordinate system information of the scanning equipment. This information is the basis for subsequent spatial registration; (2) Convert gnss.csv to 97-2003 Excel format file, extract the three columns of longitude, latitude and elevation data in the list to form an Excel file to be read; (3) Use Add XY Data in ArcGIS software to load the Excel file, read the longitude, latitude, and elevation values ​​and directly assign them to the GCS_China_Geodetic_Coordinate_System_2000 geographic coordinate system, and convert them to generate a vector file with a geographic coordinate system; next, you need to convert from the geographic coordinate system to the projection coordinate system. Determine the projection coordinate system according to the longitude and latitude of the acquisition area, and then use the projection transformation tool to convert the vector file with the geographic coordinate system into a projection coordinate system vector file (select the corresponding belt number according to the longitude value); finally, open the attribute table in the vector file display window to add fields, and perform geometric calculations (Calculate Geometry) on the projection coordinates of the XX and YY fields in the scanning device trajectory. The Z elevation field remains unchanged, and select dBASE Table to generate a *.dbf format file; (4) For the *.dbf file in step (3), open it with Excel and edit it, retaining only the projection coordinates and elevation fields, and then export it as a *.csv file; (5) Use CloudCompare software to load the *.csv file in step (4), select the corresponding X, Y, and Z fields, and read them to generate three-dimensional linear trajectory data with projection coordinates, and output them as *.las / laz format files; (6) Use Trimble RealWorks software to read the 3D linear trajectory in step (5) and the 3D laser point cloud data of the first station at the same time, and use the 3D linear trajectory as a reference to manually align the 3D laser point cloud data of the first station. Finding the starting point, end point and main feature point positions is the key to improving the accuracy of manual alignment. Visually identify at least 3 points of overlap between the first station laser point cloud feature objects and the 3D trajectory in space. Based on this, manually fine-tune the overall matching degree between the first station 3D laser point cloud and the spatial trajectory from different angles, taking into account both horizontal and vertical accuracy to match the entire 3D spatial data. Since the 3D point cloud is aligned with the linear 3D trajectory, the inspection accuracy will be lower, but it can be controlled within 0.9-1.1 meters; (7) The subsequent point cloud splicing is based on the point cloud data after the spatial registration of the first station in step (6), and the three-dimensional ground feature of the overlapping segment is used for semi-automatic registration. After manually registering two segments of three-dimensional laser point cloud data with overlapping ground feature, after achieving almost overlapping effect in the horizontal and vertical directions, select the "Refine" option to calculate and optimize the point cloud of the overlapping segment of the two stations, that is, semi-automatic registration, which can effectively improve the registration accuracy, and the inspection accuracy can be controlled at 0.3-0.4 meters.

[0011] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A cave 3D laser point cloud spatial registration method with invalid fixed solution, characterized by: The method is: after collecting the cloud data of the first station and processing it in the office, if the fixed solution of the spatial coordinates is found to be invalid, it is necessary to manually align the trajectory coordinates and point cloud data collected by the RTK of this station, including the following steps: (1) In the first station scanning data outside the cave, find project_data\algorithm\gnss.csv in the folder named by date + time. This csv format file contains the trajectory coordinates, elevation values ​​and geographic coordinate system information of the scanning equipment. This information is the basis for subsequent spatial registration; (2) Convert gnss.csv to an Excel file, extract the three columns of data (longitude, latitude, and elevation) from the list to form an Excel file to be read; (3) Use Add XY Data in ArcGIS software to load the Excel file, read the longitude, latitude, and elevation values ​​and directly assign them to the GCS_China_Geodetic_Coordinate_System_2000 geographic coordinate system, and convert them to generate a vector file with a geographic coordinate system; next, it is necessary to convert from the geographic coordinate system to the projection coordinate system, determine the projection coordinate system according to the longitude and latitude of the acquisition area, and then use the projection transformation tool to convert the vector file with the geographic coordinate system into a projection coordinate system vector file; finally, open the attribute table in the vector file display window to add fields, perform geometric calculations and assign values ​​to the projection coordinates of the XX and YY fields in the scanning device trajectory, and keep the Z elevation field unchanged. Select dBASE Table as the output to generate a *.dbf format file; (4) For the *.dbf file in step (3), open it with Excel and edit it, retaining only the projection coordinates and elevation fields, and then export it as a *.csv file; (5) Use CloudCompare software to load the *.csv file in step (4), select the corresponding X, Y, and Z fields, and read them to generate three-dimensional linear trajectory data with projection coordinates, and output them as *.las / laz format files; (6) Use Trimble RealWorks software to read the 3D spatial linear trajectory and the first station 3D laser point cloud data in step (5) at the same time, and use the 3D spatial linear trajectory as a reference to manually align the first station 3D laser point cloud data, that is, visually identify at least 3 points of overlap between the first station laser point cloud feature objects and the 3D trajectory in space, and then manually fine-tune the overall matching degree between the first station 3D laser point cloud and the spatial trajectory from different angles, taking into account both horizontal and vertical accuracy to match the entire 3D spatial data, and the inspection accuracy is controlled at 0.9-1.1 meters; (7) The subsequent point cloud stitching is based on the point cloud data after spatial registration at the first station in step (6). The three-dimensional terrain features of the overlapping segments are used for semi-automatic registration. The two segments of three-dimensional laser point cloud data with overlapping terrain features are manually registered. After almost overlapping in the horizontal and vertical directions, the point clouds of the overlapping segments of the two stations are optimized and calculated, i.e., semi-automatic registration. The inspection accuracy is controlled at 0.3-0.4 meters.

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