Research and Application of the Obstacle Avoidance System for High-Speed Railway Tunnel Lining Inspection Train Based on Integrated 3D LiDAR and 2D Camera Machine Vision Technology  
[PERSON]  
1  
[PERSON]  
[PERSON] and [PERSON]  
1  
Footnote 1: [[https://github.com](https://github.com)]([https://github.com](https://github.com))  
[PERSON] et al. [11] improved the reliability of autonomous vehicles with a 3D LiDAR-based obstacle detection and tracking method. [PERSON], [PERSON], et al. [12] proposed real-time vehicle detection on occupancy grid maps, contributing to a dependable environmental model for autonomous driving. Moreover, deep learning has achieved significant progress in point cloud recognition, as exemplified by PointNet [13], PointNet++ [14], and PointCNN [15]. However, challenges, such as point cloud density, small objects, occlusions, and noise, persist [13; 14; 15]. In machine vision image recognition, [PERSON], [PERSON], et al. [16] improved obstacle detection with a 3D object detection method based on stereo imagery. Deep learning-based image recognition methods, such as YOLO [17], SSD [18], DeepLab [19], and PSPNet [20], have been extensively applied, enabling obstacle detection through pixel classification. In addition, [PERSON] et al. [21; 22; 23; 24] applied the advanced methods of deep learning to the field of road detection and also achieved remarkable results. Nonetheless, complex environments with varying illumination and shadows impact the accuracy of machine vision-based technologies. As for the application of digital twin technology, it offers the potential for testing obstacle detection algorithms in virtual environments through data collection, modeling, and simulation [25; 26]. However, challenges such as data quality, model reliability and accuracy, data privacy, and security remain [27; 28], necessitating further research in this field. Although obstacle detection technology has been widely applied in fields such as autonomous driving and intelligent transportation, it still lacks effective applications in high-speed rail tunnel non-destructive testing. Currently, for vehicle-mounted GPR to inspect high-speed rail tunnel lining, it is crucial to maintain a continuous operating distance of 10 cm \(\pm\) 2 cm from the lining surface [29; 30]. Real-time and accurate detection of visual obstacles, such as the \"anchor section\" of the railway contact network (Figure 1a), surface cables of the lining (Figure 1b), and protruding objects (Figure 1c), is necessary to avoid damage to the detection device and to ensure the quality of the collected data. Therefore, research on intelligent obstacle avoidance for vehicle-mounted GPR detection devices is of utmost importance. Currently, the collision risk warning for vehicle-mounted GPR detection equipment primarily relies on 2D scanning, which results in blind spots in the detection and warning of critical collision risks posed by the protrusion size of obstacles. Additionally, real-time manual attention to front-end risks is required during practical operations. This, combined with the limited recognition capacity of the human eye, hinders the improvement of detection efficiency. Figure 1: “Obstacles” in the detection of high-speed railway tunnel. (**a**) Anchor section of the railway contact network. (**b**) Surface cables of the lining. (**c**) protruding objects. In response to these issues, this study conducts comprehensive research on upgrading the obstacle avoidance system. Firstly, through the analysis of overall technical parameter requirements and structure, the fundamental preconditions for module development are proposed. Then, by integrating research on ORB-SLAM3 and Normal Distribution Transform (NDT) techniques for point cloud alignment and thickening method study, obstacle risk detection and localization based on safety boundary models, and multi-modal data fusion technologies, collision risks for the robotic arm are timely warned.