Precise and reliable localization of intelligent vehicles for safe driving

Abstract

Autonomous driving technology has become a spotlight in recent years. Of all the factors related to autonomous driving, safety should be first considered. A safe global trajectory should be planned at beginning and local safe trajectory should be planned according to the situations in real time. Due to this, the intelligent vehicles must know where they are in real time to do the next control steps. In this paper, a high-precision localization framework for intelligent vehicles is proposed. A vertical low-cost LIDAR is used for mapping and live data collection. High-precision maps are generated by projecting laser scans along the survey trajectory produced by trajectory filter. When localizing, an improved matching method particle Iterative Closet Point is proposed. Using this particle ICP, not only the matching precision is improved, but also the computing time decreases remarkably, which helps to make the algorithm real-time. Decimeter-level precision can be achieved by the validation of experiments. The results show much benefit for safe driving by this Monte Carlo framework.