LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain

TLDR

The paper proposes LeGO‑LOAM, a lightweight, ground‑optimized lidar odometry and mapping method for real‑time six‑degree‑of‑freedom pose estimation on ground vehicles. LeGO‑LOAM achieves this by segmenting point clouds to remove noise, extracting planar and edge features, and applying a two‑step Levenberg‑Marquardt optimization that uses these features to compute the six‑degree‑of‑freedom transformation, while a ground‑plane‑based segmentation and a SLAM integration further reduce drift. Experiments show that LeGO‑LOAM runs in real time on low‑power embedded hardware, matches or surpasses LOAM’s accuracy on variable‑terrain datasets, and reduces pose drift when integrated into a SLAM framework on the KITTI dataset.

Abstract

We propose a lightweight and ground-optimized lidar odometry and mapping method, LeGO-LOAM, for realtime six degree-of-freedom pose estimation with ground vehicles. LeGO-LOAM is lightweight, as it can achieve realtime pose estimation on a low-power embedded system. LeGO-LOAM is ground-optimized, as it leverages the presence of a ground plane in its segmentation and optimization steps. We first apply point cloud segmentation to filter out noise, and feature extraction to obtain distinctive planar and edge features. A two-step Levenberg-Marquardt optimization method then uses the planar and edge features to solve different components of the six degree-of-freedom transformation across consecutive scans. We compare the performance of LeGO-LOAM with a state-of-the-art method, LOAM, using datasets gathered from variable-terrain environments with ground vehicles, and show that LeGO-LOAM achieves similar or better accuracy with reduced computational expense. We also integrate LeGO-LOAM into a SLAM framework to eliminate the pose estimation error caused by drift, which is tested using the KITTI dataset.