A recent study tested and validated a sophisticated sensor system using two LiDAR devices, an IMU, and a camera installed in a locomotive’s driver’s cab. To ensure accurate data collection in tunnels without GPS signal, a software synchronization approach based on PTP was employed.
The study focused on map consistency analysis in subway tunnels using a high-precision algorithm based on LiDAR data. Results showed that the algorithm outperformed mainstream open-source alternatives, demonstrating high accuracy and robustness in tunnel scenarios.
Localization accuracy analysis was conducted to evaluate train positioning in tunnels without using GNSS. The study utilized LiDAR data to detect unique markers in the tunnel, achieving a positioning accuracy of under 0.15m with loop closure detection based on these markers.
Time analysis of the system showed that the proposed algorithm achieved real-time localization requirements for subway trains in tunnels, with a total runtime of 41 milliseconds. The system’s computational capability demonstrated redundancy, suggesting potential for optimization to further reduce computational demands.
Overall, the study showcased a comprehensive sensor system and algorithm designed for high-precision mapping and localization in subway tunnels. The results indicated that the system could accurately map tunnel environments, maintain trajectory consistency across different LiDAR devices, and achieve real-time localization requirements for subway trains, laying the groundwork for future research and optimization of the algorithm.
Source
Note: The image is for illustrative purposes only and is not the original image of the presented article.
