Dense dust and long, repetitive corridors frequently cause conventional visual and LiDAR SLAM to drift or fail in subterranean coal mines. FIRE-LIVWO addresses this by tightly coupling 4D millimeter-wave radar and wheel odometry with visual-inertial and LiDAR inputs within an iterated error-state Kalman filter. Leveraging Doppler constraints and online observability analysis, the framework dynamically adjusts modality weights when specific sensors degrade. Real-world mine trials yielded an average localization error of 5.677 meters.
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