PaGO-LOAM: Robust Ground-Optimized LiDAR Odometry

Abstract

Numerous researchers have conducted studies to achieve fast and robust ground-optimized LiDAR odometry methods for terrestrial mobile platforms. In particular, groundoptimized LiDAR odometry usually employs ground segmentation as a preprocessing method. This is because most of the points in a 3D point cloud captured by a 3D LiDAR sensor on a terrestrial platform are from the ground. However, the effect of the performance of ground segmentation on LiDAR odometry is still not closely examined. In this paper, a robust ground-optimized LiDAR odometry framework is proposed to facilitate the study to check the effect of ground segmentation on LiDAR SLAM based on the state-of-the-art (SOTA) method. By using our proposed odometry framework, it is easy and straightforward to test whether ground segmentation algorithms help extract well-described features and thus improve SLAM performance. In addition, by leveraging the SOTA ground segmentation method called Patchwork, which shows robust ground segmentation even in complex and uneven urban environments with little performance perturbation, a novel ground-optimized LiDAR odometry is proposed, called PaGO-LOAM. The methods were tested using the KITTI odometry dataset. PaGO-LOAM shows robust and accurate performance compared with the baseline method. Our code is available at https://github.com/url-kaist/AlterGround-LeGO-LOAM

Robust Lidar-Inertial Odometry with Ground Condition Perception and Optimization Algorithm for UGV

“…As the ground occupies a large part of the lidar scan of the surrounding environment, it is an non-negligible environmental element for LIO systems used in UGV. Despite the general methods mentioned above, several works embedded with ground-optimized methods are proposed to preserve the qualities of state estimation, especially for UGVs [ 25 , 26 , 27 , 28 , 29 ]. LeGO-LOAM [ 25 ] is a loosely coupled lidar-inertial odometry that utilizes the technique of ground optimization in various terrain.…”