DROID-SLAM in the Wild
We present a robust, real-time RGB SLAM system that handles dynamic environments by leveraging differentiable Uncertainty-aware Bundle Adjustment. Traditional SLAM methods typically assume static scenes, leading to tracking failures in the presence of motion. Recent dynamic SLAM approaches attempt to address this challenge using predefined dynamic priors or uncertainty-aware mapping, but they remain limited when confronted with unknown dynamic objects or highly cluttered scenes where geometric mapping becomes unreliable. In contrast, our method estimates per-pixel uncertainty by exploiting multi-view visual feature inconsistency, enabling robust tracking and reconstruction even in real-wo...
| Authors | Moyang Li, Zihan Zhu, Marc Pollefeys et al. |
|---|---|
| Primary category | cs.CV |
| Published | 19 March 2026 |
| Last updated at source | 19 March 2026 |
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