SenseWeather: Multimodal Sensing for Robust Robotic Perception under Extreme Conditions
Robotic perception must remain reliable when extreme environmental conditions severely degrade the primary RGB observation. Although robotic platforms commonly carry complementary sensors such as depth and infrared, much of the modern perception stack remains RGB-centric, creating a mismatch between multimodal onboard sensing and pretrained downstream models. We study whether complementary sensing can be used upstream to recover a task-compatible RGB observation that preserves existing perception capabilities without modifying or retraining the downstream stack. To this end, we introduce SenseWeather, a controlled multimodal benchmark for robotic perception under severe rain, snow, dust, ...
| Authors | Fucai Ke, Sukai Huang, Junyi Hao et al. |
|---|---|
| Primary category | cs.RO |
| Published | 8 October 2026 |
| Last updated at source | 8 October 2026 |
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