Intelligent Vehicle Self-Localization Based on Double-Layer Features and Multilayer LIDAR

High-precision self-localization is one of the basic functions of intelligent vehicles. Moreover, location information is necessary prior information for tasks such as behavioural decision-making and path planning. Recently, the technology of map-based localization has been widely concerned due to its accuracy. A prior map for localization stores one or more environmental features extracted by on-board sensors. These features represent fundamental information about the ground environment such as curbs and lane markings, or the vertical environment such as building outlines. In order to enhance the robustness and positioning accuracy, this paper uses two layers of features to express the environment: one is a bottom layer composed of ground and curb features, and the other is an upper layer composed of a 2D point cloud with vertical features. Firstly, a novel collision-based detection method is proposed to extract the curb features, and a vertical-projection-based detection algorithm is used to detect vertical feature points from multilayer LIDAR. Then, the fusion of a multi-frame feature point cloud is used to represent the rich and complete information on the environment of the intelligent vehicle. Finally, the Monte Carlo localization algorithm is used to obtain an optimal estimate of the vehicle position. The experimental results indicate that the proposed localization algorithm can realize localization accuracy.