Real-Time, Environmentally-Robust 3D LiDAR Localization

TL;DR

This paper introduces PointLocalization, a real-time 3D LiDAR-based localization method that is robust to environmental changes, capable of long-term operation, and achieves high accuracy and speed in dynamic settings.

Contribution

It presents a novel map division and cloud registration approach that enhances robustness and reduces registration time, along with fusion of odometers for improved accuracy.

Findings

Achieves localization at over 20 Hz after fusion.

Maintains 10 cm accuracy in changing environments.

Operates successfully over 2000 km and six months in a factory setting.

Abstract

Localization, or position fixing, is an important problem in robotics research. In this paper, we propose a novel approach for long-term localization in a changing environment using 3D LiDAR. We first create the map of a real environment using GPS and LiDAR. Then, we divide the map into several small parts as the targets for cloud registration, which can not only improve the robustness but also reduce the registration time. PointLocalization allows us to fuse different kinds of odometers, which can optimize the accuracy and frequency of localization results. We evaluate our algorithm on an unmanned ground vehicle (UGV) using LiDAR and a wheel encoder, and obtain the localization results at more than 20 Hz after fusion. The algorithm can also localize the UGV in a 180-degree field of view (FOV). Using an outdated map captured six months ago, this algorithm shows great robustness, and the test results show that it can achieve an accuracy of 10 cm. PointLocalization has been tested for a period of more than six months in a crowded factory and has operated successfully over a distance of more than 2000 km.