SAFEVPR: Patch-Based Verification with Empirical FDR Control for Cross-Condition Sequence Visual Place Recognition
Ha Sier, Jiaqiang Zhang, Zhuo Zou, Xianjia Yu, Tomi Westerlund
Sequence-based visual place recognition (VPR) for loop closure and robot relocalization must decide whether the retrieved top-1 candidate should be accepted. Conformal calibration can set this accept/reject threshold, but its finite-sample guarantees require exchangeability between calibration and deployment, a requirement that cross-condition deployment violates. We therefore claim no formal guarantee under condition shift: this paper instead asks which verification score and calibration design keep a target false-discovery rate (FDR) empirically satisfied across realistic appearance change. We present SAFEVPR, a non-trainable pipeline that replaces the backbone cosine similarity with a mutual-nearest-neighbour (MNN) patch score from frozen DINOv2 features, and flat Learn-Then-Test (LTT) calibration with per-bin Clopper-Pearson thresholds. Across 23 cross-condition ground-vehicle setups (Oxford RobotCar, NCLT, St Lucia; three frozen backbones), SAFEVPR meets the FDR target on 23/23 setups at alpha = 0.10 (mean accepted FDR 0.014, mean TPR 0.75) and on 22/23 at alpha = 0.05 with 22 retaining non-trivial coverage (best cosine variant, cosine+LTT: 22/23 valid but 17 non-trivial); under leave-one-dataset-out calibration, validity drops to 20/23 with 14 retaining useful coverage (strongest cosine baseline: 16/23 and 10), and on texture-poor scenery (Nordland) the verifier is uninformative and the system abstains entirely, as it does everywhere at alpha = 0.01; we report these as limitations. In a pose-graph relocalization study, gating loop-closure constraints reduces erroneous constraints by 29.6x and cuts the median trajectory error from 95.5 m to 9.7 m. Code is available at https://github.com/TIERS/SafeVPR.
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