2D Range Flowbased Odometry fusing LiDAR and IMU This paper describes a 2D odometry method fusing LiDAR and IMU data to estimate ground vehicle motion and performing better in long corridor scenarios and moving objecting scenarios. The method builds on range flow constraint for every scanned point and rejects moving objects by fusing IMU data. Besides, our method applies the graphical model to solve the optimization problem that adds IMU constraint to the odometry system. Experiment results demonstrate that our approach is able to perform as well as other stateofart approaches and better than these approaches in long corridor scenarios and moving objecting scenarios. Besides we test our approach on an embedded system which makes it suitable for those lowcost robotic applications that require planar odometry.

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.