Title: MambaMatch: SLAM Front-End Feature Matching with State Space Models
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: SLAM, Feature Matching, State Space Models

Abstract: SLAM (Simultaneous Localization and Mapping) systems depend on front-end components for feature detection and matching. Traditional methods use handcrafted or learning-based features, but both have limitations in robustness and efficiency. We propose a new SLAM front-end framework that fuses recent State Space Models (SSMs), specifically the Mamba architecture, with transformer-based attention. Our method exploits the linear efficiency of SSMs for visual feature processing and the global modeling of attention. By integrating these techniques, we achieve better feature matching on challenging datasets while keeping computation efficient. The fusion strategy adaptively balances local relationships from Mamba and global dependencies from attention. Experiments show our approach surpasses state-of-the-art methods in feature matching precision and recall, especially in scenes with repetitive patterns, lighting and viewpoint shifts.