Abstract
In order to meet the needs of intelligent perception of the driving environment, a point cloud registering method based on 3D NDT-ICP algorithm is proposed to improve the modeling accuracy of tunneling roadway environments. Firstly, Voxel Grid filtering method is used to preprocess the point cloud of tunneling roadways to maintain the overall structure of the point cloud and reduce the number of point clouds. After that, the 3D NDT algorithm is used to solve the coordinate transformation of the point cloud in the tunneling roadway and the cell resolution of the algorithm is optimized according to the environmental features of the tunneling roadway. Finally, a kd-tree is introduced into the ICP algorithm for point pair search, and the Gauss–Newton method is used to optimize the solution of nonlinear objective function of the algorithm to complete accurate registering of tunneling roadway point clouds. The experimental results show that the 3D NDT algorithm can meet the resolution requirement when the cell resolution is set to 0.5 m under the condition of processing the point cloud with the environmental features of tunneling roadways. At this time, the registering time is the shortest. Compared with the NDT algorithm, ICP algorithm and traditional 3D NDT-ICP algorithm, the registering speed of the 3D NDT-ICP algorithm proposed in this paper is obviously improved and the registering error is smaller.
Highlights
The environment of the tunneling face in coal mines is complex and the intelligence degree is low, making it difficult for tunneling equipment to perceive the work environment intelligently [1]
Because it is difficult to accurately judge the registration effect of the four algorithms through pictures, this paper introduces the root mean square error to judge the point cloud registration error
A point cloud registering method for tunneling roadways based on 3D
Summary
The environment of the tunneling face in coal mines is complex and the intelligence degree is low, making it difficult for tunneling equipment to perceive the work environment intelligently [1]. Sensors can be used to acquire the environmental point cloud data of tunneling roadways and establish the environmental point cloud map, which is convenient for guiding and carrying out the excavation and mining work [2,3]. At present, improving the accuracy of the underground environment map of coal mines is the primary difficulty in the intelligent construction of the tunneling face [4]. Technologies such as 3D scanning and DT (Digital Twins) have received extensive attention in the industrial field.
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