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arXiv · 2610.16143

All-St3R SLAM: Real-time RGB Dynamic SLAM with Pixel-wise Motion Masking

Yewon Kim·Paul Hongsuck Seo·Sang Chul Ahn
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Abstract

SLAM (Simultaneous Localization and Mapping) is a core technology in autonomous systems that enables them to estimate their own position and build a map of unknown environments simultaneously. SLAM systems in dynamic environments are of great practical importance. However, existing dynamic SLAM approaches still have several limitations like over-removal and high computational costs. To address these limitations, we propose a real-time RGB Dynamic SLAM framework that precisely removes moving objects at the pixel level without relying on predefined labels or segmentation models. Our system combines pixel motion predicted by ego-motion using DUSt3R with point tracking from All-Tracker, removing only actually moving pixels by analyzing discrepancies between the two motion sources. Unlike object-level masking methods, our approach eliminates only currently moving regions, allowing temporarily static parts to be utilized for tracking. Operating at approximately 10 FPS, the system achieves real-time performance on standard 30 FPS video streams by subsampling every 3 frames. Relying solely on RGB inputs, this significantly improves upon state-of-the-art dense dynamic SLAM approaches (0.5-3 FPS), presenting a highly practical solution.

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Yewon Kim, Paul Hongsuck Seo, Sang Chul Ahn. 2026-10-12. All-St3R SLAM: Real-time RGB Dynamic SLAM with Pixel-wise Motion Masking. https://arxiv.org/abs/2610.16143

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