FoundationSLAM: Dense Visual SLAM with Depth Foundation Models
Published:Dec 31, 2025 17:57
•1 min read
•ArXiv
Analysis
This paper introduces FoundationSLAM, a novel monocular dense SLAM system that leverages depth foundation models to improve the accuracy and robustness of visual SLAM. The key innovation lies in bridging flow estimation with geometric reasoning, addressing the limitations of previous flow-based approaches. The use of a Hybrid Flow Network, Bi-Consistent Bundle Adjustment Layer, and Reliability-Aware Refinement mechanism are significant contributions towards achieving real-time performance and superior results on challenging datasets. The paper's focus on addressing geometric consistency and achieving real-time performance makes it a valuable contribution to the field.
Key Takeaways
- •Proposes FoundationSLAM, a novel monocular dense SLAM system.
- •Leverages depth foundation models to improve accuracy and robustness.
- •Introduces a Hybrid Flow Network, Bi-Consistent Bundle Adjustment Layer, and Reliability-Aware Refinement mechanism.
- •Achieves real-time performance (18 FPS) and superior results on challenging datasets.
Reference
“FoundationSLAM achieves superior trajectory accuracy and dense reconstruction quality across multiple challenging datasets, while running in real-time at 18 FPS.”