Maximum Clique-Based Floorplan Association for Robust Multi-Session Stereo SLAM in Challenging Indoor Environments

Abstract

Existing multi-session visual simultaneous localization and mapping (SLAM) systems struggle severely to achieve robust localization and map merging under extreme viewpoint and illumination variations, particularly when handling completely opposite viewpoints and drastic day-night lighting changes. These challenges stem largely from the limited viewpoint/illumination invariance of conventional low-level visual features and their inability to capture a global structural context. In this paper, we make the critical observation that a life-long floorplan not only encodes rich geometric and semantic information—serving as a robust high-level structural representation—but is also inherently more robust to severe viewpoint and illumination variations than purely visual data. Building on this insight, we propose a novel hierarchical framework for multi-session SLAM that integrates a floorplan-based map as a global feature to achieve robust indoor localization and map merging under drastic viewpoint and illumination shifts. In particular, we innovatively formulate floorplan association as a maximum clique problem augmented with trajectory data to achieve robust floorplan-level global localization. We further introduce a novel coarse-to-fine localization and map merging strategy that seamlessly integrates floorplan alignment, multistage point cloud registration, and feature matching, fully leveraging the macro-level stability of global features and the micro-level precision of local features to achieve keyframe-level fine localization. Extensive experiments on both public and self-collected datasets demonstrate that our method consistently outperforms state-of-the-art (SOTA) approaches reliant solely on low-level visual or geometric features. Crucially, it delivers superior accuracy and robustness even in the face of completely opposite viewpoints and extreme day–night illumination changes. This work underscores the promise of fusing macro-level floorplan representations with conventional SLAM frameworks to advance long-term, robust indoor localization and map merging under the most challenging conditions.