A Compact LiDAR Odometry and Mapping with Dynamic Removal

Real-time state estimation and mapping is a funda-mental function of unmanned systems. In this paper, we propose a compact 3D Lidar odometry and mapping method (C-LOAM) for real-time 6-DOF pose estimation and map construction for unmanned ground vehicles. Our approach firstly remove dynamic objects from the environment by comparing differences between two consecutive range images. We then extract ground points from the range image. The remaining points are segmented in the range image to obtain robust feature points for the registration stage. After performing two-stage scan-matching, we use the ex-tracted ground points to impose ground constraints and leverage the local feature sub map for loop closure detection. By employing various data reuse strategies, we minimize memory consumption, creating a compact and accurate system. Experiments on multiple publicly available datasets demonstrate that our system provides more accurate localization and higher-quality maps compared to other state-of-the-art methods.