Establishment of a Coal Mine Roadway Model Based on Point Cloud Feature Matching

Abstract

A coal mine roadway is a longitudinally limited space with curves and branches, low illumination and high humidity, a large amount of dust, and an unstructured terrain environment. Traditional ICP algorithms have the defects of slow convergence speed and it is easy to fall into local optimums. While the NDT algorithm in the NDT + ICP algorithm has high registration efficiency, poor stability, and low registration accuracy, which are not suitable for point clouds with noise and a large amount of data. By calculating the FPFH value, the detailed description of the point cloud will be greatly increased to increase the robustness and accuracy Therefore, a feature registration method based on the FPFH + ICP algorithm is proposed to reduce the modeling error of excavation roadways and meet the requirements of intelligent excavation. First, outliers caused by dust are treated by the Euclidean clustering point cloud segmentation method, and then the calculation of the normal vector in the FPFH feature descriptor is optimized based on extracting key points from the roadway structure. The surface normal vector of each key point and its neighborhood point is estimated according to the measured point and its neighborhood point. The initial coordinate transformation matrix of a point cloud of an excavated roadway is obtained by the SAC-IA algorithm and transferred to the ICP algorithm. Finally, KD-tree is introduced into the ICP algorithm to accelerate the search speed of corresponding point pairs, and the Gauss–Newton method is used to optimize the solution of the nonlinear objective function of the algorithm to complete accurate registration of point clouds in an excavation roadway.

CM-YOLOv8: Lightweight YOLO for Coal Mine Fully Mechanized Mining Face

“…Traditional approaches employ image-processing techniques such as edge detection, texture analysis, and shape descriptors. These techniques extract manually designed features from images, which are subsequently utilized for target detection and classification [ 13 ]. Presently, machine-learning-based methods, such as SVM (Support Vector Machine) [ 14 ] or Random Forest [ 15 ], are more widely applied.…”

A Fast Registration Method for MEMS LiDAR Point Cloud Based on Self-Adaptive Segmentation

“…In recent years, new methods have been constantly emerging, and application scenarios have been expanding [25][26][27][28][29][30][31][32]. Among these, an improved ICP algorithm that combines RANSAC, Intrinsic Shape Signature (ISS), and 3DSC is proposed in [30].…”