论文标题
基于LIDAR数据的细分和本地化使用开放式街道地图为乡村道路
LIDAR data based Segmentation and Localization using Open Street Maps for Rural Roads
论文作者
论文摘要
准确的姿势估计是所有移动机器人必须具有的基本能力，才能在给定的环境中稳健地穿越。就像人类一样，这种能力取决于机器人对给定场景的理解。对于自动驾驶汽车（AV），事先创建的详细的3D地图被广泛用于增强感知能力，并根据当前的传感器测量值估算姿势。然而，这种方法不太适合稀疏连接并覆盖较大地区的农村社区。为了应对在农村环境中将车辆定位的挑战，本文介绍了农村道路场景的数据集，以及使用LIDAR Point Clouds快速分割道路的方法。与Open Street Maps（OSM）的道路网络信息共同使用的分段点云用于姿势估算。我们提出了两个测量模型，这些模型与在OSM上定位的最新方法进行了比较，以进行跟踪以及全球本地化。结果表明，所提出的算法能够以6.5米的平均精度在2平方公里的区域内估计姿势。
Accurate pose estimation is a fundamental ability that all mobile robots must posses in order to traverse robustly in a given environment. Much like a human, this ability is dependent on the robot's understanding of a given scene. For Autonomous Vehicles (AV's), detailed 3D maps created beforehand are widely used to augment the perceptive abilities and estimate pose based on current sensor measurements. This approach however is less suited for rural communities that are sparsely connected and cover large areas. To deal with the challenge of localizing a vehicle in a rural setting, this paper presents a data-set of rural road scenes, along with an approach for fast segmentation of roads using LIDAR point clouds. The segmented point cloud in concert with road network information from Open Street Maps (OSM) is used for pose estimation. We propose two measurement models which are compared with state of the art methods for localization on OSM for tracking as well as global localization. The results show that the proposed algorithm is able to estimate pose within a 2 sq. km area with mean accuracy of 6.5 meters.
