TRAIL: Trajectory-Aware Visual Place Recognition against Unordered Databases

• URL: http://arxiv.org/abs/2609.07373v1
• Date: Mon, 07 Sep 2026 11:49:42 GMT
• Title: TRAIL: Trajectory-Aware Visual Place Recognition against Unordered Databases
• Abstract summary: We formalize a task that exploits given a query sequence, localize the final image against an unordered reference database.<n>We propose TRAIL, a principled framework that combines learned functions for visual similarity and for camera-motion consistency.<n>A lightweight post-processing layer atop any pre-trained VPR backbone, TRAIL improves a state-of-the-art baseline by up to 8.3 percentage points.
• Score: 3.7384509727711923
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Modern Visual Place Recognition (VPR) methods excel on standard benchmarks yet remain brittle in feature-poor environments. By treating each query image in isolation, they discard the sequential context in any real trajectory. We formalize a task that exploits this context: given a query sequence, localize the final image against an unordered reference database -- which, unlike sequence-to-sequence methods, requires no sequential structure in the database. We propose TRAIL (TRajectory-Aware Image Localization), a principled framework based on Conditional Random Fields (CRF) that combines learned functions for visual similarity and for camera-motion consistency, refining a distribution over candidate references as each query arrives. A lightweight post-processing layer atop any pre-trained VPR backbone, TRAIL improves a state-of-the-art baseline by up to 8.3 percentage points on our primary benchmark, transfers to unseen datasets without retraining, and delivers its largest gains where visual cues are scarce.

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