AF-RLIO adaptively switches between LiDAR-inertial and radar-inertial odometry based on a feature-ratio degradation detector and uses chi-square GPS outlier weighting, showing lower APE in tunnels and smoke than the tested baselines.
A benchmark for lidar sensors in fog: Is detection breaking down?,
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AF-RLIO: Adaptive Fusion of Radar-LiDAR-Inertial Information for Robust Odometry in Challenging Environments
AF-RLIO adaptively switches between LiDAR-inertial and radar-inertial odometry based on a feature-ratio degradation detector and uses chi-square GPS outlier weighting, showing lower APE in tunnels and smoke than the tested baselines.