SenseWeather: Multimodal Sensing for Robust Robotic Perception
Overview
SenseWeather introduces a benchmark for robotic perception under extreme weather. Fusion-to-RGB techniques enhance detection performance across models without retraining. Results show improved robustness through multimodal sensing in rain, snow, and fog conditions. (2026-10-14).
Generated from attributed reports · Updated 3 hours ago
Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
No current evidence-backed claims. Missing information remains not reported.
Report timeline
Follow attributed reports and material updates.
- arXiv Robotics — research abstractsSenseWeather: Multimodal Sensing for Robust Robotic Perception under Extreme Conditions
This research introduces SenseWeather, a controlled benchmark for robotic perception under extreme weather conditions such as rain, snow, and fog. The paper proposes Fusion-to-RGB techniques using sensor fusion to generate task-compatible RGB observations, improving downstream perception tasks without retraining models. Results show that multimodal sensing enhances perception robustness, with improvements in detection performance across various models.
Event coverage history
There is not enough continuous observation data to show a trend.
Timezone · UTC
Article dates follow your selected timezone. Briefing editions use Hong Kong time (UTC+8).