All-St3R SLAM: Real-time RGB Dynamic SLAM with Pixel-wise Motion Masking
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.
ProofPaper ↗
Key points
- 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.
- 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.
- 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.
Sources (1)
- [1]All-St3R SLAM: Real-time RGB Dynamic SLAM with Pixel-wise Motion MaskingarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 12, 04:04 AM
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.
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.
Extractive summary: sentences quoted from the sources.