Robust Localization for Intelligent Vehicles Based on Pole-Like Features Using the Point Cloud

Localization in the complex urban environment is an open problem for current methods. The occlusion from dynamic objects, such as vehicles and pedestrians, degenerates the precision of the localization result. This article proposes a pole-like feature-based localization framework to solve this problem. Pole-like objects, such as posts of lamps or traffic sign and tree trunks, widely exist in the urban environment and are robust to occlusion, as they are usually higher than the objects on the road. First, this type of feature is extracted from the point cloud by a robust clustering algorithm. Then, the features from different frames of data are stitched to generate a feature map. For online localization, a Monte Carlo localization (MCL) framework is used to fuse the vehicle motion data and the map-matching result. An improved version of iterative closest point (ICP) that is specifically designed for the pole-like feature association is used for map matching based on the state of every particle. With the MCL scheme, localization is robust to the local minimum or robot kidnapping problem. Experimental results in the real urban environment demonstrate the precision and robustness of the proposed method, with mean absolute errors less than 0.20 m and 0.5°. The results also show that the proposed method outperforms some state-of-the-art localization methods in the complex urban environment.
Note to Practitioners
—There are some works using features from the 3-D point cloud, e.g., corners, planes, and reflectance, for robot localization. Instead of the abstract features, this article presents an object-feature-based localization scheme. We propose a novel pole-like object extraction algorithm based on the spatial distribution of the 3-D points. This algorithm can extract most types of pole-like objects in the urban environment. As these objects are highly distinct from other types of objects in their surroundings, localization is achieved by associating the pole-like features in the map and the features detected in real time through maximizing the likelihood. The whole system is verified with data collected in the real world, which indicates that its accuracy can fulfill the requirements of autonomous driving. The limitation of the proposed method is that it highly depends on one specific type of feature, which may not work well in the rural environment. In future research, we will address this problem by incorporating more types of semantic features for localization.