We look at estimating occluded or otherwise missing regions of point-cloud terrain data using Gaussian Process Regression (GPR). The ability to estimate missing data allows motion planners to create more aggressive and cost efficient paths. Previous approaches in applying GPR to terrain estimation have large computational complexity or trade off accuracy of far-field data with computational complexity. We present Multiresolution Partitioned Gaussian Process Regression (MPGPR) to address these weaknesses. MPGPR improves upon past results by 1) using a quadtree structure to intuitively partition data in order to speed up parameter optimization times, as well as 2) using a hierarchical structure to capture long range data dependencies while preserving local detail. This hierarchical structure allows the method to perform well on practical datasets that may have nonuniform densities. Our method is evaluated on many artificially generated terrains as well as real data obtained from the Mars Curiosity Rover.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Multiresolution partitioned Gaussian process regression for terrain estimation


    Contributors:
    Zhang, Clark (author) / Ono, Masahiro (author) / Lanka, Ravi (author)


    Publication date :

    2018-03-01


    Size :

    1796809 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Multiresolution terrain modelling from multisensor data

    Wright, J. / Sturdevant, K. / Morrison, J. | NTRS | 2002


    Multiresolution-Based 3-D Terrain Estimation Algorithms for Complex Urban Environments

    Vergara, Pedro / Tiwari, Madhar / Prazenica, Richard J. et al. | AIAA | 2019


    Multiresolution-Based 3-D Terrain Estimation Algorithms for Complex Urban Environments

    Vergara, Pedro / Tiwari, Madhar / Prazenica, Richard J. et al. | TIBKAT | 2019


    Using octrees for multiresolution terrain model operations

    Wright, J. R. / Sturdevaut, K. / Morrison, J. | NTRS | 2001


    Vehicle consumption estimation via calibrated Gaussian Process regression

    Randon, Mathieu / Quost, Benjamin / Boudaoud, Nassim et al. | IEEE | 2022