A robust submap-based road shape estimation via iterative Gaussian process regression

摘要

Road shape estimation is important for the safe driving of intelligent vehicles. The common road shape models such as line/parabola, spline and clothoid are lacking of flexibility in various urban traffic scenes. In this paper, a robust road shape model which consists of multiple overlapped submaps is proposed. Each individual submap is represented by a smooth curve generated through Gaussian process(GP). To estimate parameters of a GP submap, a framework involving pre-processing, pose correction, road shape regression and map updating/creating is proposed. Pose correction is achieved by fusion of vehicle motion model and simplified GP-based observation model. Road shape regression is used to extract a coarse road shape. Map updating/creating is used to adapt to the new coming data and generates refined road shape. A robust iterative Gaussian process regression(iGPR) is utilized in both road shape regression and map updating/creating. Extensive experimental results show the efficiency of the proposed method.