There are a range of small-size robots that cannot afford to mount a three-dimensional sensor due to energy, size or power limitations. However, the best localization and mapping algorithms and object recognition methods rely on a three-dimensional representation of the environment to provide enhanced capabilities. Thus, in this work we propose a method to create a dense three-dimensional representation of the environment by fusing the output of a KSLAM algorithm with predicted point clouds. We demonstrate with quantitative and qualitative results the advantages of our method, focusing in three different measures: localization accuracy, densification capabilities and accuracy of the resultant three-dimensional map.


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    Title :

    Map Slammer: Densifying Scattered KSLAM 3D Maps with Estimated Depth


    Additional title:

    Advs in Intelligent Syst., Computing



    Conference:

    Iberian Robotics conference ; 2019 ; Porto, Portugal November 20, 2019 - November 22, 2019



    Publication date :

    2019-11-20


    Size :

    12 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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