SLAMFormer-$\infty$: Infinite SLAM Transformer for Unbounded Frontend and Backend Processing
Zhijian Fang, Weicheng Zheng, Yijun Yuan, Weibang Wang, Zhuoguang Chen, Chang Sun, Junhao Huang, Kenan Li, Minghui Qin, Hang Zhao
Abstract
We introduce the Infinite SLAM Transformer (SLAMFormer-$\infty$), the first geometric transformer capable of supporting both long-range frontend and backend processing without an explicit distance bound. Instead of relying on a first-frame-anchored formulation, SLAMFormer-$\infty$ employs memory conditions to define flexible coordinate systems and scales for input frames, enabling more expressive structural conditioning. Built upon this formulation, the frontend preserves efficient local computation, while the backend jointly optimizes long-range trajectories and scene geometry in a globally consistent manner. Experimental results demonstrate that SLAMFormer-$\infty$ achieves superior or highly competitive performance in both trajectory estimation and scene reconstruction across large-scale datasets. Notably, SLAMFormer-$\infty$ generalizes to extremely long trajectories, successfully operating on sequences exceeding $17\mathrm{km}$.
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Classified with taxonomy v2 on Wed, 2 Sept 2026.