论文标题
大声笑：3D点云图中仅使用激光雷达的进程和本地化
LOL: Lidar-Only Odometry and Localization in 3D Point Cloud Maps
论文作者
论文摘要
在本文中，我们处理了在城市环境中驾驶的配备激光雷达的车辆的前进和本地化问题，那里的预制目标图存在于本地化。在我们的问题公式中，为了纠正仅激光雷达的探测器的累积漂移，我们应用了一种位置识别方法，以检测在线3D点云和先验的离线映射之间的几何相似位置。在拟议的系统中，我们通过补充其优点，将最先进的光探光算法与最近提出的3D点段匹配方法整合在一起。此外，我们提出了其他增强功能，以减少在线点云和目标映射之间的错误匹配数，并在检测到良好匹配时完善位置估计错误。我们在几个不同的长度和环境的Kitti数据集上演示了拟议的LOL系统的实用性，在这种情况下，在每种情况下，重新定位精度和车辆轨迹的精度都大大提高，同时仍然能够保持实时性能。
In this paper we deal with the problem of odometry and localization for Lidar-equipped vehicles driving in urban environments, where a premade target map exists to localize against. In our problem formulation, to correct the accumulated drift of the Lidar-only odometry we apply a place recognition method to detect geometrically similar locations between the online 3D point cloud and the a priori offline map. In the proposed system, we integrate a state-of-the-art Lidar-only odometry algorithm with a recently proposed 3D point segment matching method by complementing their advantages. Also, we propose additional enhancements in order to reduce the number of false matches between the online point cloud and the target map, and to refine the position estimation error whenever a good match is detected. We demonstrate the utility of the proposed LOL system on several Kitti datasets of different lengths and environments, where the relocalization accuracy and the precision of the vehicle's trajectory were significantly improved in every case, while still being able to maintain real-time performance.
