Grid map registration is an important field in mobile robotics. Applications in which multiple robots are involved benefit from multiple aligned grid maps as they provide an efficient exploration of the environment in parallel. In this paper, a normal distribution transform (NDT)-based approach for grid map registration is presented. For simultaneous mapping and localization approaches on laser data, the NDT is widely used to align new laser scans to reference scans. The original grid quantization-based NDT results in good registration performances but has poor convergence properties due to discontinuities of the optimization function and absolute grid resolution. This paper shows that clustering techniques overcome disadvantages of the original NDT by significantly improving the convergence basin for aligning grid maps. A multi-scale clustering method results in an improved registration performance which is shown on real world experiments on radar data.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Clustering improved grid map registration using the normal distribution transform


    Contributors:


    Publication date :

    2015-06-01


    Size :

    511192 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Scan Registration using the Normal Distributions Transform with Region Growing Clustering for Point-Sampled 3D Surfaces (AIAA 2014-0976)

    Ahuja, S. / Waslander, S. / American Institute of Aeronautics and Astronautics | British Library Conference Proceedings | 2014



    Improved registration arm

    IAN REDDALL / GARY ANDREW WALKER | European Patent Office | 2023

    Free access

    NDT RC: Normal Distribution Transform Occupancy 3D Mapping With Recentering

    Courtois, Hugo / Aouf, Nabil / Ahiska, Kenan et al. | IEEE | 2024