A LiDAR-Inertial SLAM Tightly-Coupled with Dropout-Tolerant GNSS Fusion for Autonomous Mine Service Vehicles

TL;DR

This paper presents a robust LiDAR-inertial SLAM system with GNSS fusion designed for autonomous mine vehicles, effectively handling GNSS dropouts and long-term operation challenges to maintain accurate localization.

Contribution

It introduces a tightly-coupled LiDAR-inertial SLAM framework with loop closure re-initialization and dropout-tolerant GNSS fusion, specifically tailored for mine environments.

Findings

Achieves meter-level accuracy during GNSS dropouts.

Demonstrates robustness to data loss and observation degeneracy.

Maintains accurate localization over tens of minutes in tunnel environments.

Abstract

Multi-modal sensor integration has become a crucial prerequisite for the real-world navigation systems. Recent studies have reported successful deployment of such system in many fields. However, it is still challenging for navigation tasks in mine scenes due to satellite signal dropouts, degraded perception, and observation degeneracy. To solve this problem, we propose a LiDAR-inertial odometry method in this paper, utilizing both Kalman filter and graph optimization. The front-end consists of multiple parallel running LiDAR-inertial odometries, where the laser points, IMU, and wheel odometer information are tightly fused in an error-state Kalman filter. Instead of the commonly used feature points, we employ surface elements for registration. The back-end construct a pose graph and jointly optimize the pose estimation results from inertial, LiDAR odometry, and global navigation satellite system (GNSS). Since the vehicle has a long operation time inside the tunnel, the largely accumulated drift may be not fully by the GNSS measurements. We hereby leverage a loop closure based re-initialization process to achieve full alignment. In addition, the system robustness is improved through handling data loss, stream consistency, and estimation error. The experimental results show that our system has a good tolerance to the long-period degeneracy with the cooperation different LiDARs and surfel registration, achieving meter-level accuracy even for tens of minutes running during GNSS dropouts.