An Enhanced LiDAR-Inertial SLAM System for Robotics Localization and Mapping

TL;DR

This paper presents an improved LiDAR-inertial SLAM system for unmanned ground vehicles, enhancing robustness and accuracy through advanced filtering and loop closure techniques, validated by extensive real-world experiments.

Contribution

The work introduces two key innovations: a more robust particle swarm filter-based LiDAR SLAM and new loop closure methods for global optimization, improving system performance.

Findings

Enhanced robustness of LiDAR SLAM system.

Improved localization accuracy through loop closure.

Validated effectiveness in complex real-world environments.

Abstract

The LiDAR and inertial sensors based localization and mapping are of great significance for Unmanned Ground Vehicle related applications. In this work, we have developed an improved LiDAR-inertial localization and mapping system for unmanned ground vehicles, which is appropriate for versatile search and rescue applications. Compared with existing LiDAR-based localization and mapping systems such as LOAM, we have two major contributions: the first is the improvement of the robustness of particle swarm filter-based LiDAR SLAM, while the second is the loop closure methods developed for global optimization to improve the localization accuracy of the whole system. We demonstrate by experiments that the accuracy and robustness of the LiDAR SLAM system are both improved. Finally, we have done systematic experimental tests at the Hong Kong science park as well as other indoor or outdoor real complicated testing circumstances, which demonstrates the effectiveness and efficiency of our approach. It is demonstrated that our system has high accuracy, robustness, as well as efficiency. Our system is of great importance to the localization and mapping of the unmanned ground vehicle in an unknown environment.