SLAM-Former: Putting SLAM into One Transformer

Abstract:We present SLAM-Former, a neural approach that integrates full SLAM capabilities into a single transformer. Similar to traditional SLAM systems, SLAM-Former comprises both a frontend and a back-end that operate in tandem. The frontend processes sequential monocular images in real-time for incremental mapping and tracking, while the backend performs global refinement to ensure a geometrically consistent result. This alternating execution allows the frontend and back-end to mutually promote one another, enhancing overall system performance. Comprehensive experimental results demonstrate that SLAM- Former achieves superior or highly competitive performance compared to state-of-the-art dense SLAM methods.

Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)

arXiv:2509.16909 [cs.CV]

(or arXiv:2509.16909v2 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2509.16909 Focus to learn more arXiv-issued DOI via DataCite

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