Visual Place Recognition

Visual Place Recognition studies how robots and other systems identify previously visited locations from images, supporting localization, navigation, and loop closure in SLAM. Methods range from global descriptors learned with CNNs or vision transformers to local feature matching and geometric verification; sequence models also use motion and temporal context. Multimodal approaches combine images with depth, LiDAR, event cameras, language, or aerial imagery, and are evaluated across driving, indoor, campus, and natural-scene settings using retrieval and pose metrics. A central challenge is generalizing beyond familiar urban conditions while keeping systems efficient and reliable when scenes change, viewpoints differ, or visual matches are ambiguous.