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-world environments. The proposed system achieves state-of-the-art camera poses and scene geometry in cluttered dynamic scenarios while running in real time at around 10 FPS. Code and datasets are available at https://github.com/MoyangLi00/DROID-W.git.
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Moyang Li
claimed_verified
2026-03-23T09:53:46.167Z
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Moyang Li
moyangli
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moyangli
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Zihan Zhu
claimed_verified
2026-03-23T09:53:42.493Z
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ZIHAN ZHU
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user
zzh2000
69bddc823fd3fef6237a63f5
Marc Pollefeys
69bddc823fd3fef6237a63f6
Daniel Barath
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-world environments. The proposed system achieves state-of-the-art camera poses and scene geometry in cluttered dynamic scenarios while running in real time at around 10 FPS. Code and datasets are available at https://github.com/MoyangLi00/DROID-W.git.
A real-time RGB SLAM system uses differentiable uncertainty-aware bundle adjustment to handle dynamic environments by estimating per-pixel uncertainty from multi-view visual features, achieving state-of-the-art performance in cluttered scenes while maintaining real-time processing.
RGB SLAM Uncertainty-aware Bundle Adjustment dynamic environments multi-view visual features per-pixel uncertainty real-time processing camera poses scene geometry