LIO-Fusion: Reinforced LiDAR Inertial Odometry by Effective Fusion with GNSS/Relocalization and Wheel Odometry

• Published in: IEEE robotics and automation letters Vol. 8; no. 3; pp. 1 - 8
• Main Authors: Wu, Wenhong, Zhong, Xunyu, Wu, Dongjie, Chen, Bushi, Zhong, Xungao, Liu, Qiang
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.03.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Autonomous navigation; Coders; Feature extraction; Global navigation satellite system; Hazardous areas; Laser radar; Lidar; Localization; mapping; Modules; Reliability; Reliability aspects; robotics in hazardous fields; Sensor fusion; Sensor systems; Sensors; SLAM; State estimation; Wheels
• ISSN: 2377-3766; 2377-3766
• DOI: 10.1109/LRA.2023.3240372

Reliable state estimation is a prerequisite for autonomous robot navigation in complex environments. In this work, we present LIO-Fusion, a reinforced LiDAR inertial odometry system that optimally fuses GNSS/relocalization and wheel odometry to provide accurate and robust 6-DoF movement estimation under challenging perceptual conditions. LIO-Fusion formulates multi-source sensors fusion based on factor graph, allowing a multitude of relative and absolute measurements which may be degraded, disturbed or even inaccessible. In the LIO-Fusion system, online initialization consists of point cloud feature extraction and matching, IMU preintegration, encoder integration, GNSS calibration and prior-map relocalization. Then, its global reinforcement module detects the reliability of GNSS/relocalization to obtain healthy GNSS/relocalization factors, whereas the local reinforcement module uses a sub-factor graph to fuse prior estimation results for the reinforced local odometry factor. Finally, the basic LiDAR/IMU factors, healthy GNSS/relocalization factors and reinforced local odometry factor are jointly used to constrain the system state in the main factor graph such that low-drift odometry under LiDAR degradation can be reliably obtained and corrected globally. We extensively evaluated the real-time LIO-Fusion system by real-world experiments, and compared its performance to other state-of-the-art methods on large-scale datasets collected in the urban and hazardous environments. Results have shown that LIO-Fusion yielded high precision localization and mapping accuracy as well as robustness to sensor failures.