From Underground Mines to Offices: A Versatile and Robust Framework for Range-Inertial SLAM

TL;DR

This paper introduces LG-SLAM, a versatile and robust range-inertial SLAM framework adaptable to various sensors and environments, achieving high accuracy and real-time performance through graph optimization and GPU acceleration.

Contribution

The paper presents LG-SLAM, a flexible SLAM system that integrates range, inertial, and GNSS data with a novel submap and loop closure approach, adaptable to diverse settings with minimal tuning.

Findings

Achieves below 20 cm average error across diverse environments

Operates at LiDAR frame rate with online loop closing

Outperforms state-of-the-art SLAM algorithms in accuracy and robustness

Abstract

Simultaneous Localization and Mapping (SLAM) is an essential component of autonomous robotic applications and self-driving vehicles, enabling them to understand and operate in their environment. Many SLAM systems have been proposed in the last decade, but they are often complex to adapt to different settings or sensor setups. In this work, we present LiDAR Graph-SLAM (LG-SLAM), a versatile range-inertial SLAM framework that can be adapted to different types of sensors and environments, from underground mines to offices with minimal parameter tuning. Our system integrates range, inertial and GNSS measurements into a graph-based optimization framework. We also use a refined submap management approach and a robust loop closure method that effectively accounts for uncertainty in the identification and validation of putative loop closures, ensuring global consistency and robustness. Enabled by a parallelized architecture and GPU integration, our system achieves pose estimation at LiDAR frame rate, along with online loop closing and graph optimization. We validate our system in diverse environments using public datasets and real-world data, consistently achieving an average error below 20 cm and outperforming other state-of-the-art algorithms.