[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82851-en":3,"doc-seo-82851-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},82851,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Trajectory-Anchor Optimization for Overconfident Thermal Visual Place Recognition: Zero-Leakage OOD Auditing and Kidnapped-Robot Recovery","Modern thermal visual place recognition (TIR-VPR) frontends based on foundation-model descriptors deliver strong closed-set retrieval, yet fail under out-of-distribution or unmapped conditions due to an overconfident forced-matching mode that yields locally consistent but false loop candidates. Classical multi-hypothesis tracking can reduce spatial ambiguity but introduces prohibitive real-time latency. Trajectory-Anchor Optimization (TAO) replaces MHT with batched SE(2) Procrustes alignment solved via tensor-vectorized, single-invocation batched SVD, enabling strictly bounded per-frame computation. Passive geometric consistency further acts as a conservative rejector. Zero-leakage auditing shows a micro-scale separation limit near 5m, while a macroscopic convergence basin around 10m reliably suppresses catastrophic topological breaks.","arXiv :2607 .04745v 1 [ cs .RO] 6 Jul 2026  \nTrajectory-Anchor Optimization for Overconfident Thermal Visual Place Recognition: Zero-Leakage OOD Auditing and Kidnapped-Robot  \nRecovery  \nZhiyuan Lu Kanji Tanaka  \nJuly 7, 2026  \nAbstract  \nModern thermal visual place recognition (TIR-VPR) frontends, particularly foundation-model-based descriptors such as AnyThermal-SALAD, exhibit remarkable retrieval performance as closed-set candidate generators. However, their structural vulnerability lies in an overconfident forced-matching failure mode: under out-ofdistribution (OOD) or unmapped conditions, these deep models generate highly plausible, locally consistent yet false loop candidates without a noticeable drop in similarity scores. While classical multi-hypothesis tracking (MHT) backends can mitigate such spatial ambiguities by maintaining divergent trajectory beliefs, their exponential hypothesis management overhead introduces prohibitive computational latency, fundamentally violating real-time robotic constraints. To bridge this gap, we present Trajectory-Anchor Optimization (TAO) . Crucially, to counter the combinatorial challenge of evaluating a large pool of parallel hypotheses (e.g. , K = 100), TAO compresses the multi-view temporal verification into a batched SE(2) Procrustes alignment problem. By leveraging tensor-level vectorization and single-invocation batched Singular Value Decomposition (SVD), this formulation entirely bypasses the dynamic tree expansion and memory allocation overhead inherent to MHT, guaranteeing a strictly bounded per-frame execution loop of O (KN) while maintaining rigid multi-view geometric consistency. While this passive temporal verification effectively bounds the search space, we show that incorporating rigid multi-view geometric consistency establishes a risk-averse, conservative rejecting layer capable of filtering unstructured spatial hallucinations without requiring active exploration strategies. Under a strict zero-leakage evaluation protocol, we demonstrate that while a purely passive geometric backend cannot mathematically separate metric localization errors from highly coherent hallucinations at a micro-scale (5m) due to local visual ambiguities, TAO serves as a highly efficient, lightweight fail-safe filter at a macro-scale (large map-out scenarios) . The visual foundation model’s overconfident hallucinations within a tight 5m radius often possess a locally consistent geometry that deceives rigid multi-view alignment. However, beyond this micro-scale threshold, the K = 100 disparate retrieval hypotheses disperse spatially across the global map. This spatial dispersion breaks the rigid temporal co-visibility constraint within the sliding window (N = 20), causing the joint optimization residual to escalate sharply. Consequently, while the passive backend exhibits an information-theoretic limit atthe sub-5m resolution, it establishes a distinct macroscopic convergence basin (10m) where multi-view geometric consistency reliably isolates catastrophic topological breaks and suppresses critical false acceptances, defining a transparent operational boundary for preventing graph corruption.  \n1 Introduction  \nThermal infrared (TIR) cameras are highly attractive for long-term autonomous navigation because they remain informative across severe diurnal illumination changes. Driven by deep foundation models, recent TIR visual place recognition (VPR) frontends, such as AnyThermal-SALAD [11], have substantially improved closed-set retrieval accuracy. Yet, an architectural paradox remains: a stronger retrieval frontend does not automatically guarantee a safer SLAM backend. Due to the low-texture nature of thermal imagery, these models suffer from a critical flaw of overconfident forced matching—consistently yielding false loop candidates backed by falsely high similarity scores that exhibit a deceptive, smooth continuity even when traversing completely unmapped out-of-distribution (OOD) environments.","cbCaifnyXmFJqDE8","https://ap.wps.com/l/cbCaifnyXmFJqDE8","pdf",1950025,1,11,"English","en",105,"# Introduction\n## Overconfident forced-matching failure mode in thermal VPR\n## Limits of multi-hypothesis tracking for real-time robotics\n## Trajectory-Anchor Optimization (TAO) as a bounded multi-view backend\n# Zero-leakage auditing and operational boundary\n## Micro-scale indistinguishability near 5m\n## Macroscopic convergence basin around 10m\n## Filtering false accepts to prevent graph corruption","[{\"question\":\"What problem does TAO address in thermal visual place recognition?\",\"answer\":\"TAO targets overconfident forced-matching failures in thermal VPR, where models generate false loop candidates that remain locally consistent and may not show a similarity-score drop under OOD or unmapped conditions.\"},{\"question\":\"Why are traditional multi-hypothesis tracking backends considered unsuitable for real-time deployment?\",\"answer\":\"Multi-hypothesis tracking can require an exploding hypothesis tree during extended unmapped periods, creating intensive computation and collapsing real-time performance.\"},{\"question\":\"How does TAO achieve bounded computation while verifying multiple hypotheses?\",\"answer\":\"TAO evaluates K parallel trajectories simultaneously by converting temporal verification into batched SE(2) Procrustes alignment problems, solved with tensor-level vectorization and a single batched SVD, avoiding dynamic tree expansion and memory overhead.\"}]",1784183427,28,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"trajectory-anchor-optimization-for-overconfident-thermal-visual-place-recognition-zero-leakage-ood-auditing-and-kidnapped-robot-recovery","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/trajectory-anchor-optimization-for-overconfident-thermal-visual-place-recognition-zero-leakage-ood-auditing-and-kidnapped-robot-recovery/82851/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-17","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does TAO address in thermal visual place recognition?","Question",{"text":75,"@type":76},"TAO targets overconfident forced-matching failures in thermal VPR, where models generate false loop candidates that remain locally consistent and may not show a similarity-score drop under OOD or unmapped conditions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why are traditional multi-hypothesis tracking backends considered unsuitable for real-time deployment?",{"text":80,"@type":76},"Multi-hypothesis tracking can require an exploding hypothesis tree during extended unmapped periods, creating intensive computation and collapsing real-time performance.",{"name":82,"@type":73,"acceptedAnswer":83},"How does TAO achieve bounded computation while verifying multiple hypotheses?",{"text":84,"@type":76},"TAO evaluates K parallel trajectories simultaneously by converting temporal verification into batched SE(2) Procrustes alignment problems, solved with tensor-level vectorization and a 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