{"ID":5937295,"CreatedAt":"2026-07-07T03:14:33.014478982Z","UpdatedAt":"2026-07-09T07:52:46.28543944Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.04764","arxiv_id":"2607.04764","title":"SLAM: Structured and Localized Analytic Manifold Adaptation for Lifelong VPR","abstract":"Visual Place Recognition (VPR) in lifelong deployment requires continuous adaptation to new environments without catastrophic forgetting. In this paper, we propose SLAM, a Structured and Localized Analytic Manifold adaptation framework. Our framework elegantly unifies uncertainty-aware smoothing via Unscented transformation, topological space partitioning through a Gaussian Mixture Model (GMM), and $H_\\infty$ robust bound optimization into a singular, unified closed-form analytical recursion. Exhaustive ablation studies demonstrate that while the synergistic combination of uncertainty smoothing and localized mapping (U+G configuration) achieves the state-of-the-art nominal accuracy of 27.5%, the full deployment of the $H_\\infty$ bound does not require an architectural split; rather, it introduces a mathematically guaranteed minimax robust bound. This formulation enables the system to seamlessly modulate the intrinsic trade-off between nominal placement precision and worst-case disturbance attenuation through a single regularization parameter.","short_abstract":"Visual Place Recognition (VPR) in lifelong deployment requires continuous adaptation to new environments without catastrophic forgetting. In this paper, we propose SLAM, a Structured and Localized Analytic Manifold adaptation framework. Our framework elegantly unifies uncertainty-aware smoothing via Unscented transform...","url_abs":"https://arxiv.org/abs/2607.04764","url_pdf":"https://arxiv.org/pdf/2607.04764v1","authors":"[\"Kenta Tsukahara\",\"Kanji Tanaka\",\"Rai Hisada\"]","published":"2026-07-06T07:57:26Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.CV\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}