SFPR: Structural Fingerprinting for LiDAR-to-OpenStreetMap Place Recognition

Cognitive neuroscience research indicates that human spatial navigation relies not merely on abstract global map representations, but rather on anchoring local observations to spatially stable, decision-relevant landmarks to effectively align the current scene with the cognitive map. Inspired by this mechanism, we propose SFPR, a structural fingerprint-based LiDAR-to-OpenStreetMap (OSM) place recognition framework. This framework goes beyond the traditional coarse retrieval paradigm that relies exclusively on global descriptor similarity and further introduces fine-grained discrimination based on structural fingerprints to achieve more accurate place recognition. Specifically, we propose a structural fingerprint-aware attention mechanism that utilizes sparse but spatially stable anchors as saliency prompts, directing the network's attention toward potential fingerprint regions while dynamically generating global descriptors. Subsequently, we formulate the extraction and matching of structural fingerprints as an alternating optimization problem guided by this fingerprint-aware attention. Through iterative optimization, we extract structural fingerprints with high fingerprint-aware attention and geometric consistency from anchors. These structural fingerprints are further fed back into the training stage as geometric priors, effectively suppressing false matches characterized by similar global features but contradictory local structures. Experiments demonstrate that SFPR effectively overcomes the retrieval bottleneck in macroscopically homogeneous scenes, achieving a relative improvement of 30.20\% in Top-1 Recall@1m compared to the current state-of-the-art method. Code are publicly available at https://anonymous.4open.science/r/SFPR.