基于LIDAR数据的农村公路开放式街道地图分割和定位
LIDAR data based Segmentation and Localization using Open Street Maps for Rural Roads
摘要：
准确的位姿估计是所有移动机器人必须具备的基本能力，以便在给定的环境中鲁棒地穿越。就像人类一样，这种能力取决于机器人对给定场景的理解。对于自动驾驶车辆（AV），预先创建的详细3D地图被广泛用于增强感知能力并基于当前传感器测量来估计姿态。然而，这种方法不太适合于联系稀少和覆盖面积大的农村社区。为了应对在农村环境中定位车辆的挑战，本文提出了一个农村道路场景的数据集，以及使用激光雷达点云快速分割道路的方法。与来自开放街道地图（OSM）的道路网络信息相一致的分割的点云用于姿态估计。我们提出了两种测量模型，与最先进的方法进行比较，定位OSM跟踪以及全球定位。实验结果表明，该算法能够在2平方米的范围内估计出机器人的姿态。平均精度为6.5米。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
