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.
Multimodal features and accurate place recognition with robust optimization for lidar–visual–inertial slam,
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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.