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.


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    Title :

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


    Contributors:
    Wang, Di (author) / Xue, Jianru (author) / Cui, Dixiao (author) / Zhong, Yang (author)


    Publication date :

    2017-06-01


    Size :

    626045 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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