PRIOR-SLAM: Enabling Visual SLAM for Loop Closure Under Large Viewpoint Variations

Existing visual simultaneous localization and mapping (SLAM) systems struggle with loop closure under significant viewpoint variations, such as revisiting the same place orthogonally or oppositely. This limitation primarily stems from the lack of viewpoint invariance in both macroscopic place definition and microscopic feature description. Based on the crucial insight that geometric information is more viewpoint-invariant than visual information, we create map segments via leveraging the position and coplanarity distribution of map elements within the map constructed by monocular SLAM to overcome the limitations of frame-based place definition and extract perspective-invariant ORB (PRIOR) features via accounting for local surface perspective distortions to enhance perspective invariance without the need for costly perspective invariance estimation or additional depth data. We further utilize map segments and PRIOR features to hierarchically detect and correct loop closures with a coarse-to-fine geometric consistency check. We integrate our novelties into the prevalent SLAM framework, thereby proposing PRIOR-SLAM, which achieves state-of-the-art performance in feature matching and retrieval, visual place recognition, and loop closure under large viewpoint changes.