Hierarchical Neighborhood Based Precise Localization for Intelligent Vehicles in Urban Environments

High-precision localization has drawn more and more attention in recent research of intelligent vehicle systems and autonomous robot navigation technology. In most methods, the approaches are only effective in some specific situations. In other words, these methods can only perform well with obvious features, like tall building walls, road curbs, etc. In this paper, a novel framework for precise localization of autonomous vehicle applying to different scenes especially some typical urban environments is proposed. The main procedures of this method include mapping and localization. During mapping process, inertial measurement unit, odometry, and high-precision GPS are fused together with the data sensed by LIDAR, a high-precision map that could provide global position and pose is generated using rolling window. When localizing, live laser data align with the prior-map. A particle filter based point cloud matching method is utilized here. Based on this, a hierarchical localizing method is proposed, which is more accurate and faster than the original matching method. With this method, the sampling guided by proposal density is propagated upward every hierarchy. Besides that, some accelerating algorithms are utilized to make this approach real time. Finally, decimeter-level localization is achieved in different environments, which is proven by some experiments.