FLoTS-SLAM: Optical Flow-Derived Uncertainty for Monocular Dynamic SLAM with Two-Stage Optimization
Monocular simultaneous localization and mapping (SLAM) remains challenging in dynamic environments.
ProofPaper ↗
Key points
- Recent approaches estimate per-pixel uncertainty from rendering residuals or multiview feature inconsistency to identify dynamic regions.
- Accordingly, we present a monocular RGB SLAM system for dynamic scenes.
- Specifically, a motion uncertainty estimation method is proposed to derive uncertainty from the residual between optical flow and camera motion induced flow, directly reflecting object motion.
- Moreover, we provide a quantitative evaluation of dynamic masks and analyze how these masks affect the downstream tracking and reconstruction of dynamic objects.
Sources (1)
- [1]FLoTS-SLAM: Optical Flow-Derived Uncertainty for Monocular Dynamic SLAM with Two-Stage OptimizationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 16, 02:07 PM
Monocular simultaneous localization and mapping (SLAM) remains challenging in dynamic environments.
Recent approaches estimate per-pixel uncertainty from rendering residuals or multiview feature inconsistency to identify dynamic regions.
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