A Real-Time Method for Railway Track Detection and 3D Fitting Based on Camera and LiDAR Fusion Sensing

Railway track detection, which is crucial for train operational safety, faces numerous challenges such as the curved track, obstacle occlusion, and vibrations during the train’s operation. Most existing methods for railway track detection use a camera or LiDAR. However, the vision-based approach lac...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2024-04, Vol.16 (8), p.1441
Main Authors: Tang, Tiejian, Cao, Jinghao, Yang, Xiong, Liu, Sheng, Zhu, Dongsheng, Du, Sidan, Li, Yang
Format: Article
Language:English
Subjects:
3D fitting
Accuracy
Algorithms
Analysis
camera
Cameras
Deep learning
Environmental information
Image segmentation
LiDAR
Methods
Occlusion
Optical radar
Parameters
Railroads
railway track detection
Railway tracks
Real time
Remote sensing
Semantic segmentation
Semantics
Sensors
Vector processing (computers)
Vibrations
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Summary:Railway track detection, which is crucial for train operational safety, faces numerous challenges such as the curved track, obstacle occlusion, and vibrations during the train’s operation. Most existing methods for railway track detection use a camera or LiDAR. However, the vision-based approach lacks essential 3D environmental information about the train, while the LiDAR-based approach tends to detect tracks of insufficient length due to the inherent limitations of LiDAR. In this study, we propose a real-time method for railway track detection and 3D fitting based on camera and LiDAR fusion sensing. Semantic segmentation of the railway track in the image is performed, followed by inverse projection to obtain 3D information of the distant railway track. Then, 3D fitting is applied to the inverse projection of the railway track for track vectorization and LiDAR railway track point segmentation. The extrinsic parameters necessary for inverse projection are continuously optimized to ensure robustness against variations in extrinsic parameters during the train’s operation. Experimental results show that the proposed method achieves desirable accuracy for railway track detection and 3D fitting with acceptable computational efficiency, and outperforms existing approaches based on LiDAR, camera, and camera–LiDAR fusion. To the best of our knowledge, our approach represents the first successful attempt to fuse camera and LiDAR data for real-time railway track detection and 3D fitting.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs16081441