ITDNet jointly optimizes LiDAR restoration and place recognition via alternating task-driven training, and reports state-of-the-art retrieval accuracy in rain, snow, and fog.
Full waveform lidar for ad- verse weather conditions,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.RO 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
An Iterative Task-Driven Framework for Resilient LiDAR Place Recognition in Adverse Weather
ITDNet jointly optimizes LiDAR restoration and place recognition via alternating task-driven training, and reports state-of-the-art retrieval accuracy in rain, snow, and fog.