DeLiO: Decoupled LiDAR Odometry

Queens Maria Thomas, Oliver Wasenmüller, Didier Stricker

In: IEEE Intelligent Vehicles Symposium. IEEE Intelligent Vehicles Symposium (IV-2019) June 9-12 Paris France IEEE 2019.


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.

DeLiO_IV19.pdf (pdf, 4 MB)

German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz