L-LO: Enhancing Pose Estimation Precision via a Landmark-Based LiDAR Odometry

TL;DR

This paper introduces L-LO, a landmark-based LiDAR odometry method that improves pose estimation accuracy by leveraging environmental landmarks' overall characteristics through a two-stage process and a comprehensive landmark similarity index.

Contribution

The paper presents a novel LiDAR odometry approach that utilizes environmental landmarks' shape and dimension, enhancing pose estimation accuracy over existing point, line, or plane-based methods.

Findings

Outperforms existing LiDAR odometry solutions in accuracy

Effective landmark registration using a comprehensive similarity index

Validated on KITTI dataset and real-world UGV data

Abstract

The majority of existing LiDAR odometry solutions are based on simple geometric features such as points, lines or planes which cannot fully reflect the characteristics of surrounding environments. In this study, we propose a novel LiDAR odometry which effectively utilizes the overall exterior characteristics of environmental landmarks. The vehicle pose estimation is accomplished by means of two sequential pose estimation stages, namely, horizontal pose estimation and vertical pose estimation. To achieve effective landmark registration, a comprehensive index is proposed to evaluate the level of similarity between landmarks. This index takes into account two crucial aspects of landmarks, namely, dimension and shape in evaluating their similarity. To assess the performance of the proposed algorithm, we utilize the widely recognized KITTI dataset as well as experimental data collected by an unmanned ground vehicle platform. Both graphical and numerical results indicate that our algorithm outperforms leading LiDAR odometry solutions in terms of positioning accuracy.