AI 中文总结
研究针对基于基础模型的热视觉场所识别前端在分布外或未映射条件下的过度自信强制匹配失败问题，提出轨迹锚点优化（TAO），通过压缩多视图时间验证解决组合挑战，实现高效故障安全过滤。
AI 中文摘要
基于基础模型的现代热视觉场所识别（TIR-VPR）前端实现了出色的封闭集检索，但存在过度自信的强制匹配失败模式。在分布外（OOD）或未映射条件下，它们会生成看似合理但错误的循环候选。我们提出轨迹锚点优化（TAO）来弥合差距，通过压缩多视图时间验证解决组合挑战，在严格零泄漏评估协议下，TAO在宏观尺度上作为高效故障安全过滤器。
英文摘要
Strong mapped-region thermal visual place recognition (VPR) does not ensure safe rejection of unmapped queries. We identify and quantify this gap in AnyThermal: high Map-In retrieval accuracy coexists with confident false loop closures in Map-Out. We address it with ROUTE-MHT, a multimodal transformer guardrail. Causal motion forms a route-local candidate pool beyond the frontend's Top-$K$. Closed-form $\text{SE}(2)$ SVD verifies candidates, while frozen visual features, nine-dimensional SVD residuals, and motion proxies enter a masked multi-head transformer (MHT). Their interactions yield a contextual confidence correction to reject unsupported matches without altering geometric pose alignment. We collect an indoor thermal dataset with a physical mobile robot (Dataset-A), forming five same-day/cross-day map-query pairs; the protected interface reaches macro R@1@5m of .610/.861. Dataset-B comprises 20 map-hole scenarios derived from public STheReO-KAIST recordings. Across five scenario-held-out folds and three seeds, ROUTE-MHT reduces FPR from .116 for the SVD baseline to .061 (paired 95% CI [-.100, -.014]), while improving AUC from .945 to .967 and recall from .884 to .914. Public benchmark transfer checks on STheReO-KAIST, MS2, and IRSLAM-KRI extend the evaluation under their released metric or route-progress protocols.
Comments8 pages, 3 figures, technical report
