DeLiO: Decoupled LiDAR Odometry
This addresses LiDAR odometry for robotics and autonomous vehicles, but it appears incremental as it builds on existing methods with a novel decoupling approach.
The authors tackled the problem of LiDAR odometry by proposing DeLiO, which decouples rotation and translation estimation, achieving results comparable to state-of-the-art algorithms on the KITTI dataset.
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.