Article

LRIO: Resilient Decentralized Radar-LiDAR-Inertial Odometry for Subterranean Environments

The authors propose LRIO, a decentralized LiDAR-radar-inertial odometry framework that fuses LiDAR, FMCW radar, and inertial sensing for pose estimation in GPS-denied subterranean tunnels affected by smoke. At the core of the framework is an Adaptive Residual Thresholding (ART) mechanism, which detects sensor degradation over time and adjusts the fusion process, combined with a Robust Error-Tolerant Optimal Information Filter (RETOIF) that fuses LiDAR-inertial and multiple radar-inertial odometry estimates in a fault-aware manner.

Part of the event: Decentralized LiDAR-Radar-Inertial Odometry Framework Proposed

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