PaGO-LOAM: Robust Ground-Optimized LiDAR Odometry

TL;DR

This paper introduces PaGO-LOAM, a robust ground-optimized LiDAR odometry framework that leverages advanced ground segmentation to improve SLAM accuracy in urban environments, validated on the KITTI dataset.

Contribution

The paper proposes a novel LiDAR odometry method, PaGO-LOAM, integrating a state-of-the-art ground segmentation technique to enhance robustness and accuracy in complex urban terrains.

Findings

PaGO-LOAM outperforms baseline methods in accuracy.

Robust ground segmentation improves SLAM performance.

Validated on KITTI dataset with strong results.

Abstract

Numerous researchers have conducted studies to achieve fast and robust ground-optimized LiDAR odometry methods for terrestrial mobile platforms. In particular, ground-optimized 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. \textit{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.