Research on 3D point cloud reconstruction of railway tracks based on LiDAR-vision fusion
HE Qing
FU Bin
WANG Qihang
ZENG Chuqi
HAO Xiang
WANG Ping
YUAN Quan
Abstract：To address the limitations of single sensors,such as the inability of LiDAR to capture true color information and the low accuracy of image-based 3D reconstruction point clouds,this paper pro-poses a method for 3D track reconstruction by integrating LiDAR point cloud data and image data.First,real-time laser point cloud mapping of the track is achieved using LiDAR Inertial Odometry via Smoothing and Mapping(LIO-SAM),a tightly coupled radar-inertial odometry method.Next,the Scale Invariant Feature Transform(SIFT)algorithm is employed to extract feature points from mul-tiple images,and the geometric relationships between multi-view images are determined by matching corresponding feature points.Structure From Motion(SFM)and Multi-View Stereo(MVS)algo-rithms are then applied to locate,cluster,and generate dense image-based point clouds enriched with texture and color information of the track.Finally,plane features of the track slabs and linear features of the rails are used as reference features,and the Iterative Closest Point(ICP)algorithm is employed to merge and register the image point cloud with the LiDAR point cloud.By using the spatial position information of the LiDAR point cloud as a baseline and fusing it with the texture and color information from the image point cloud,an accurate and realistic 3D track model is obtained.Experimental results demonstrate that,compared with traditional registration methods,the improved algorithm achieves an 83.4%and 85.9%enhancement in shape parameter accuracy and nearest-neighbor point distribution,respectively.When performing target recognition on the track point cloud,the fused point cloud im-proves overall accuracy by 7.7%over the original point cloud and performs better on metrics such as average precision and mean intersection-over-union.Furthermore,the comparison between the calcu-lated track gauge and height difference from the fused point cloud and the measured data reveals an er-ror margin within 3 mm,verifying the effectiveness of the proposed 3D track point cloud reconstruc-tion method.
Keywords：railway track modelingLiDAR-vision fusiondata fusiontrack point cloud model
Publication Date：2024-10-28
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：9( 69-77 )