Elhousni Mahdi, Zhang Ziming, Huang Xinming
Department of Electrical and Computer Engineering, Worcester Polytechnic Institute, Worcester, MA 01609, USA.
Sensors (Basel). 2022 Jul 12;22(14):5206. doi: 10.3390/s22145206.
Cross-modal vehicle localization is an important task for automated driving systems. This research proposes a novel approach based on LiDAR point clouds and OpenStreetMaps (OSM) via a constrained particle filter, which significantly improves the vehicle localization accuracy. The OSM modality provides not only a platform to generate simulated point cloud images, but also geometrical constraints (e.g., roads) to improve the particle filter's final result. The proposed approach is deterministic without any learning component or need for labelled data. Evaluated by using the KITTI dataset, it achieves accurate vehicle pose tracking with a position error of less than 3 m when considering the mean error across all the sequences. This method shows state-of-the-art accuracy when compared with the existing methods based on OSM or satellite maps.
跨模态车辆定位是自动驾驶系统的一项重要任务。本研究提出了一种基于激光雷达点云与开放街道地图（OSM）的新方法，该方法通过约束粒子滤波器显著提高了车辆定位精度。OSM模态不仅提供了一个生成模拟点云图像的平台，还提供了几何约束（如道路）来改善粒子滤波器的最终结果。所提出的方法是确定性的，无需任何学习组件或标记数据。通过使用KITTI数据集进行评估，当考虑所有序列的平均误差时，它能实现精确的车辆位姿跟踪，位置误差小于3米。与基于OSM或卫星地图的现有方法相比，该方法展现出了领先的精度。
