论文标题
基于3D点云的实时铁路识别
Real-time Rail Recognition Based on 3D Point Clouds
论文作者
论文摘要
准确的铁路位置是用于安全监控的铁路支撑驾驶系统中的关键部分。激光雷达可以获得对铁路环境的3D信息的点云，尤其是在黑暗和可怕的天气条件下。在本文中，提出了一种基于3D点云的实时导轨识别方法来解决挑战，例如无序，不平坦的密度和大量的点云。首先提出了一种用于铁路点云的密度平衡的体素下采样方法，金字塔分区旨在将3D扫描区域划分为具有不同体积的体素。然后，开发了一个特征编码模块以找到最近的邻居点并为中心点汇总其本地几何特征。最后，提出了一个多尺度神经网络，以生成每个体素和导轨位置的预测结果。实验以9个序列的3D点云数据的序列进行。结果表明，该方法在检测笔直，弯曲和其他复杂拓扑导轨方面具有良好的性能。
Accurate rail location is a crucial part in the railway support driving system for safety monitoring. LiDAR can obtain point clouds that carry 3D information for the railway environment, especially in darkness and terrible weather conditions. In this paper, a real-time rail recognition method based on 3D point clouds is proposed to solve the challenges, such as disorderly, uneven density and large volume of the point clouds. A voxel down-sampling method is first presented for density balanced of railway point clouds, and pyramid partition is designed to divide the 3D scanning area into the voxels with different volumes. Then, a feature encoding module is developed to find the nearest neighbor points and to aggregate their local geometric features for the center point. Finally, a multi-scale neural network is proposed to generate the prediction results of each voxel and the rail location. The experiments are conducted under 9 sequences of 3D point cloud data for the railway. The results show that the method has good performance in detecting straight, curved and other complex topologies rails.
