Nan Zongliang, Zhu Guoan, Zhang Xu, Lin Xuechun, Yang Yingying
Laboratory of All-Solid-State Light Sources, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China.
College of Materials Science and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing 101407, China.
Sensors (Basel). 2024 May 15;24(10):3148. doi: 10.3390/s24103148.
This article presents a high-precision obstacle detection algorithm using 3D mechanical LiDAR to meet railway safety requirements. To address the potential errors in the point cloud, we propose a calibration method based on projection and a novel rail extraction algorithm that effectively handles terrain variations and preserves the point cloud characteristics of the track area. We address the limitations of the traditional process involving fixed Euclidean thresholds by proposing a modulation function based on directional density variations to adjust the threshold dynamically. Finally, using PCA and local-ICP, we conduct feature analysis and classification of the clustered data to obtain the obstacle clusters. We conducted continuous experiments on the testing site, and the results showed that our system and algorithm achieved an STDR (stable detection rate) of over 95% for obstacles with a size of 15 cm × 15 cm × 15 cm in the range of ±25 m; at the same time, for obstacles of 10 cm × 10 cm × 10 cm, an STDR of over 80% was achieved within a range of ±20 m. This research provides a possible solution and approach for railway security via obstacle detection.
本文提出了一种使用3D机械激光雷达的高精度障碍物检测算法，以满足铁路安全要求。为了解决点云中的潜在误差，我们提出了一种基于投影的校准方法和一种新颖的轨道提取算法，该算法能够有效处理地形变化并保留轨道区域的点云特征。我们通过提出一种基于方向密度变化的调制函数来动态调整阈值，解决了传统方法中涉及固定欧几里得阈值的局限性。最后，使用主成分分析（PCA）和局部迭代最近点（local-ICP），我们对聚类数据进行特征分析和分类，以获得障碍物聚类。我们在测试场地进行了连续实验，结果表明，我们的系统和算法对于尺寸为15 cm×15 cm×15 cm的障碍物，在±25 m范围内实现了超过95%的稳定检测率（STDR）；同时，对于尺寸为10 cm×10 cm×10 cm的障碍物，在±20 m范围内实现了超过80%的STDR。这项研究为通过障碍物检测实现铁路安全提供了一种可能的解决方案和方法。
