When LiDAR Fades, Radar Takes Over: LiDAR Reliability-Aware Adaptive Localization Under Airborne Obscurants
LiDAR-inertial odometry (LIO) is widely used for real-time localization and mapping, but its reliability can degrade substantially in subterranean environments affected by smoke, dust, and other airborne obscurants, whereas radar remains comparatively robust.
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Key points
- This article presents a one-class temporal learning framework for LiDAR degradation detection and reliability-aware localization under such conditions.
- A broad set of degradation-sensitive cues, including spatial, cross-modal, geometric/motion, intensity, and measurement-recency features, is evaluated to characterize changes in LiDAR sensing quality.
- The resulting LiDAR reliability estimate governs adaptive selection between LIO and radar inertial odometry (RIO), retaining LIO during reliable sensing and transferring localization authority to RIO when degradation is detected.
- Experimental results demonstrate degradation detection, adaptive modality switching, and continued localization output under severe LiDAR degradation.
Sources (1)
- [1]When LiDAR Fades, Radar Takes Over: LiDAR Reliability-Aware Adaptive Localization Under Airborne ObscurantsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 9, 04:04 PM
LiDAR-inertial odometry (LIO) is widely used for real-time localization and mapping, but its reliability can degrade substantially in subterranean environments affected by smoke, dust, and other airborne obscurants, whereas radar remains comparatively robust.
This article presents a one-class temporal learning framework for LiDAR degradation detection and reliability-aware localization under such conditions.
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