Sequential Visual Place Recognition: Exploiting Trajectory Priors for Robust Localization

Modern Visual Place Recognition (VPR) methods excel onstandard benchmarks yet remain brittle in feature-poor environments.By treating each query image in isolation, they discard the sequentialcontext in any real trajectory. We formalize a task that exploits thiscontext: given a query sequence, localize the final image against an un-ordered reference database—which, unlike sequence-to-sequence meth-ods, requires no sequential structure in the database. We propose TRAIL(TRajectory-Aware Image Localization), a principled framework basedon Conditional Random Fields (CRF) that combines learned functionsfor visual similarity and for camera-motion consistency, refining a dis-tribution over candidate references as each query arrives. A lightweightpost-processing layer atop any pre-trained VPR backbone, TRAIL im-proves a state-of-the-art baseline by up to 8.3 percentage points on ourprimary benchmark, transfers to unseen datasets without retraining, anddelivers its largest gains where visual cues are scarce.