SLAM: Structured and Localized Analytic Manifold Adaptation for Lifelong VPR
2026-07-06 • Robotics
RoboticsComputer Vision and Pattern Recognition
AI summaryⓘ
The authors present a new framework called SLAM to improve Visual Place Recognition over time without losing past knowledge. Their method combines several mathematical tools—like uncertainty smoothing, dividing spaces using Gaussian Mixture Models, and robust optimization—into one neat formula. They show that their approach balances accuracy and robustness by adjusting a single parameter, allowing the system to handle both precise localization and uncertain conditions effectively. Tests confirm that combining uncertainty smoothing and localized mapping leads to strong results, while the robustness feature adds guaranteed protection against worst-case errors.
Visual Place RecognitionLifelong LearningUnscented TransformationGaussian Mixture ModelH-infinity OptimizationManifold AdaptationRobust BoundsTopological Space Partitioning
Authors
Kenta Tsukahara, Kanji Tanaka, Rai Hisada
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.