DeLiO: Decoupled LiDAR Odometry. 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 stateoftheart algorithms.

LICFusion: LiDARInertialCamera Odometry This paper presents a tightlycoupled multisensor fusion algorithm termed LiDARinertialcamera fusion (LICFusion), which efficiently fuses IMU measurements, sparse visual features, and extracted LiDAR points. In particular, the proposed LICFusion performs online spatial and temporal sensor calibration between all three asynchronous sensors, in order to compensate for possible calibration variations. The key contribution is the optimal (up to linearization errors) multimodal sensor fusion of detected and tracked sparse edgesurf feature points from LiDAR scans within an efficient MSCKFbased framework, alongside sparse visual feature observations and IMU readings. We perform extensive experiments in both indoor and outdoor environments, showing that the proposed LICFusion outperforms the stateoftheart visualinertial odometry (VIO) and LiDAR odometry methods in terms of estimation accuracy and robustness to aggressive motions.