Research on 3D Object Detection for Quadruped Robot in Railway Maintenance Based on Improved PV-RCNN

Abstract

As the core infrastructure of national comprehensive transportation system, the safe operation of railway lines is crucial. Quadruped robots have become ideal carriers for railway autonomous inspection due to their excellent terrain adaptability. High-precision environmental perception is the core prerequisite for their autonomous operation. Aiming at the defects of traditional PV-RCNN algorithm in point cloud feature extraction for railway scenarios, this paper proposes an improved TS-PV-RCNN algorithm for 3D object detection using LiDAR point cloud. By introducing transform-equivariant strategy, dual attention mechanism, and Transformer feature extraction module, the feature extraction effect is optimized. Experiments on KITTI dataset and railway field dataset show that compared with the benchmark PV-RCNN algorithm, the average precision (AP) of railway equipment/vehicles, pedestrians, and foreign objects is improved by 1.35%, 16.83%, and 9.24% respectively under medium difficulty. The proposed algorithm provides a feasible technical scheme for autonomous inspection of railway lines.