发表机构
Department of Mechanical Engineering, Faculty of Engineering, University of Fukui（日本福井大学工学部机械工学科）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究终身视觉位置识别中连续适应新环境且无灾难性遗忘的问题，提出SLAM框架，统一多种方法为闭式解析递归，消融研究表明其组合达高精度，还能通过正则参数调节精度与抗干扰平衡。
AI 中文摘要
终身部署中的视觉位置识别（VPR）需要持续适应新环境而无灾难性遗忘。本文提出SLAM，一种结构化局部解析流形自适应框架。统一通过无迹变换的不确定性感知平滑、高斯混合模型的拓扑空间划分和H∞鲁棒边界优化为单一闭式解析递归。消融研究表明不确定性平滑与局部映射组合达27.5%的最先进标称精度，H∞边界的完全部署无需架构拆分，而是引入数学保证的极小极大鲁棒边界。此公式使系统能通过单个正则化参数无缝调节标称放置精度与最坏情况干扰衰减之间的内在权衡。
英文摘要
Visual Place Recognition (VPR) under long-term operation is essential for autonomous mobile robots. While Analytic Class-Incremental Learning (ACIL) provides memory-free ($O(1)$) task adaptation with exact forgetting immunity, applying it to lifelong VPR suffers from extreme vulnerability to non-linear domain shifts induced by environmental variations. In this work, we introduce the concept of the \textbf{ACIL-Domain (ACIL-D)}---a canonical invariant feature manifold where autocorrelation states remain locked. We resolve the domain vulnerability via Disentangled Domain Alignment (D-DA), which decouples latent features into invariant semantics within ACIL-D and variant style vectors for directional projection. Furthermore, by uncovering an algebraic isomorphism between recursive ACIL updates and Extended Kalman Filter (EKF) covariance propagation, we establish a control-theoretic framework designated as \textbf{SLAM} (\textbf{S}tructured and \textbf{L}ocalized \textbf{A}nalytic \textbf{M}anifold adaptation). SLAM integrates dynamic temperature-scaled Gaussian Mixture Models (GMM) to isolate topological non-linearities, Unscented perturbed propagation to dampen feature variations, and minimax $H_{\infty}$-robust criteria to bound worst-case noise accumulation. Empirical evaluations on the non-stationary NCLT dataset demonstrate that our proposed framework substantially outperforms existing baselines, achieving a final all-class accuracy of 27.7\% with the full SLAM framework (and up to 29.0\% with the U+H variant) while guaranteeing complete forgetting immunity.
Comments12 pages, technical report
