We present a discrete reversible jump Markov Chain Monte Carlo (rjMCMC) algorithm to build lane accurate maps by solving a partition problem. Our algorithm finds lane middle axes by 2D cubic Hermite splines least squares alignment to trajectory clusters. The steps of the rjMCMC optimization are designed to change the number of clusters and the trajectory assignment. Furthermore, we introduce our tracking approach, which is used to generate our trajectory data base by tracking vehicles in Velodyne LiDAR scans at several junctions in Hannover, Germany. We validate our results by comparing them to a manually generated ground truth map, based on LiDAR mobile mapping data, and a manually generated reference map on base of the trajectory data to evaluate our result by ignoring accuracy errors of the tracking process. Finally, we show the transferability of our approach by applying it to datasets from the map construction data base.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Discrete Reversible Jump Markov Chain Monte Carlo Trajectory Clustering


    Contributors:


    Publication date :

    2019-10-01


    Size :

    1596350 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Fitting multiple projective models using clustering-based Markov chain Monte Carlo inference

    Terzakis, G. / Culverhouse, P. F. / Bugmann, G. et al. | British Library Online Contents | 2015


    Markov Chain Monte Carlo Modular Ensemble Tracking

    Penne, T. / Tilmant, C. / Chateau, T. et al. | British Library Online Contents | 2013


    Sensitivity Analysis of Markov Chain Monte Carlo

    Millwater, Harry / Vazquez, Eric / Wu, Justin et al. | AIAA | 2010


    Markov Chain Monte Carlo for Automated Face Image Analysis

    Schönborn, S. / Egger, B. / Morel-Forster, A. et al. | British Library Online Contents | 2017


    Simultaneous Sampling for Robust Markov Chain Monte Carlo Inference

    Krpelik, Daniel / Aslett, Louis J. M. / Coolen, Frank P. A. | TIBKAT | 2021