LOL: Lidar-Only Odometry and Localization in 3D Point Cloud Maps

TL;DR

This paper introduces LOL, a lidar-only odometry and localization system that combines odometry with place recognition to improve accuracy in urban environments, demonstrating significant improvements on KITTI datasets.

Contribution

The paper presents a novel integration of lidar-only odometry with a 3D point segment matching method and enhancements to reduce false matches, improving localization accuracy.

Findings

Significant improvement in relocalization accuracy on KITTI datasets

Enhanced trajectory precision while maintaining real-time performance

Effective reduction of false matches in urban environments

Abstract

In this paper we deal with the problem of odometry and localization for Lidar-equipped vehicles driving in urban environments, where a premade target map exists to localize against. In our problem formulation, to correct the accumulated drift of the Lidar-only odometry we apply a place recognition method to detect geometrically similar locations between the online 3D point cloud and the a priori offline map. In the proposed system, we integrate a state-of-the-art Lidar-only odometry algorithm with a recently proposed 3D point segment matching method by complementing their advantages. Also, we propose additional enhancements in order to reduce the number of false matches between the online point cloud and the target map, and to refine the position estimation error whenever a good match is detected. We demonstrate the utility of the proposed LOL system on several Kitti datasets of different lengths and environments, where the relocalization accuracy and the precision of the vehicle's trajectory were significantly improved in every case, while still being able to maintain real-time performance.