Most LiDAR odometry algorithms estimate the transformation between two consecutive frames by estimating the rotation and translation in an intervening fashion. In this paper, we propose our Decoupled LiDAR Odometry (DeLiO), which – for the first time – decouples the rotation estimation completely from the translation estimation. In particular, the rotation is estimated by extracting the surface normals from the input point clouds and tracking their characteristic pattern on a unit sphere. Using this rotation the point clouds are unrotated so that the underlying transformation is pure translation, which can be easily estimated using a line cloud approach. An evaluation is performed on the KITTI dataset and the results are compared against state-of-the-art algorithms.


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    Title :

    DeLiO: Decoupled LiDAR Odometry


    Contributors:


    Publication date :

    2019-06-01


    Size :

    3949248 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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