From Retrieval Benchmarks to Positioning-Service Claims for Smart Cities: An Estimator-Aware Audit of Visual Place Recognition

Reliable positioning underpins connected and autonomous vehicles, delivery robots, public-transport monitoring and infrastructure inspection in smart cities. In urban canyons, covered roads and tunnels, satellite navigation can degrade, making camera-based visual place recognition (VPR) a fallback. Most VPR benchmarks, however, evaluate whether a correct place appears in a ranked list, whereas an operational positioning component must issue a coordinate-or-abstain with stated availability. We present an estimator-aware audit framework that tests whether retrieval results support such positioning-service claims. It separates reference-map opportunity, candidate retrieval, coordinate selection, acceptance and temporal aggregation, matching each comparison to its query population and estimator type. We apply the framework to leakage-controlled, route-conditioned KITTI driving sequences and prospectively test the frozen pipeline on Oxford RobotCar, NCLT and St Lucia traversals. Retrieval performance alone did not establish reliable coordinate output: no tested causal acceptance policy demonstrated the prespecified combination of high output coverage and low positioning risk, and the external service gate failed on all three independent datasets. Stage-wise analysis distinguished missing map opportunity from candidate-generation, candidate-selection and temporal-aggregation failures. For smart-city development, the findings show that resilient mobility infrastructure requires joint evaluation of output availability, positioning risk, map design and cross-domain robustness before deployment.