NDT Localization with 2D Vector Maps and Filtered LiDAR Scans

Maxime Escourrou, Joelle Al Hage, P. Bonnifait
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Abstract

High accuracy localization is a basic requirement for autonomous vehicles navigation. However, in urban environments, Global Navigation Satellite Systems (GNSS) suffer from Non-Line of Sight (NLoS) signals, multipath and sometimes a limited number of visible satellites, degrading the localization accuracy. Maps with georeferenced features are a means to address this issue. In this paper, an open access map with cadastral footprints of the buildings is used for localization. Buildings are stable over time and provide visible features in cities. Using 2D footprints of the buildings provides little detailed information, but when they are matched with long range omnidirectional LiDARs, a good quality estimated pose can be achieved. We present a method that uses the Normal Distributions Transform (NDT) to match several layers of a LiDAR scan with the map. A fast filtering method based on local linear regression is proposed to extract aligned points in the LiDAR scans which filters out the largest part of the outliers before applying the NDT optimization. The performance of the approach is evaluated on real data recorded with an experimental vehicle equipped with a ground truth. The results show that this approach is able to provide high accuracy consistent with autonomous navigation tasks.
NDT定位与二维矢量地图和过滤激光雷达扫描
高精度定位是自动驾驶汽车导航的基本要求。然而,在城市环境中,全球导航卫星系统(GNSS)受到非视距(NLoS)信号、多路径和有时有限数量的可见卫星的影响,降低了定位精度。带有地理参考功能的地图是解决这个问题的一种方法。本文采用建筑地籍足迹的开放获取地图进行定位。随着时间的推移,建筑是稳定的,并为城市提供了明显的特征。使用建筑物的二维足迹提供的详细信息很少,但当它们与远程全向激光雷达相匹配时,可以获得高质量的估计姿态。我们提出了一种使用正态分布变换(NDT)将激光雷达扫描的几层与地图匹配的方法。提出了一种基于局部线性回归的快速滤波方法来提取激光雷达扫描中的对齐点,该方法在应用无损检测优化之前过滤掉了大部分异常点。用装有地面真值的实验车辆记录的真实数据对该方法的性能进行了评价。结果表明,该方法能够提供符合自主导航任务的高精度定位。
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