Real-time localization is an important mission for self-driving cars and it is difficult to achieve precise pose information in dynamic environments. In this paper, a novel localization method is proposed to estimate the pose of self-driving cars using a 3D-LiDAR sensor. First, the multi-frame curb features and laser intensity features are extracted. Meanwhile, based on the high-precision curb map generated offline, obstacles on road are detected using region segmentation methods and their features are removed. Furthermore, a map-matching method is proposed to match the features to the map, a robust iterative closest point algorithm is utilized to deal with curb features along with a probability search method dealing with intensity features. Finally, two separate Kalman filters are used to fuse the low-cost global positioning systems and map-matching results. Both offline and online experiments are carried out in dynamic environments and the results demonstrate the accuracy and robustness of the proposed method.

中文翻译：

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一种基于激光雷达的高效动态环境下自动驾驶汽车定位方法

实时定位是自动驾驶汽车的一项重要任务，在动态环境中难以获得精确的姿态信息。在本文中，提出了一种使用 3D-LiDAR 传感器估计自动驾驶汽车姿态的新定位方法。首先，提取多帧路缘特征和激光强度特征。同时，基于离线生成的高精度路缘图，采用区域分割方法检测道路上的障碍物并去除其特征。此外，提出了一种地图匹配方法将特征与地图匹配，使用鲁棒迭代最近点算法处理路缘特征以及处理强度特征的概率搜索方法。最后，两个独立的卡尔曼滤波器用于融合低成本全球定位系统和地图匹配结果。离线和在线实验都是在动态环境中进行的，结果证明了所提出方法的准确性和鲁棒性。