B-ActiveSEAL: 位置推定と地図構築の不確かさを統合したスケーラブルな能動探索
B-ActiveSEAL: Scalable Uncertainty-Aware Active Exploration with Tightly Coupled Localization-Mapping
位置推定と地図構築の相互依存する不確かさを情報理論的に統合し、大規模環境でも計算可能な能動探索フレームワークを提案。探索と活用のバランスを適応的に取る。
詳しい要約
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著者: Min-Won Seo, Aamodh Suresh, Carlos Nieto-Granda, Solmaz S. Kia
分類: cs.RO
原文アブストラクト
Active robot exploration requires decision-making processes that integrate localization and mapping under tightly coupled uncertainty. However, managing these interdependent uncertainties over long-term operations in large-scale environments rapidly becomes computationally intractable. To address this challenge, we propose B-ActiveSEAL, a scalable information-theoretic active exploration framework that explicitly accounts for coupled uncertainties-from perception through mapping-into the decision-making process. Our framework (i) adaptively balances map uncertainty (exploration) and localization uncertainty (exploitation), (ii) accommodates a broad class of generalized entropy measures, enabling flexible and uncertainty-aware active exploration, and (iii) establishes Behavioral entropy (BE) as an effective information measure for active exploration by enabling intuitive and adaptive decision-making under coupled uncertainties. We establish a theoretical foundation for propagating coupled uncertainties and integrating them into general entropy formulations, enabling uncertainty-aware active exploration under tightly coupled localization-mapping. The effectiveness of the proposed approach is validated through rigorous theoretical analysis and extensive experiments on open-source maps and ROS-Unity simulations across diverse and complex environments. The results demonstrate that B-ActiveSEAL achieves a well-balanced exploration-exploitation trade-off and produces diverse, adaptive exploration behaviors across environments, highlighting clear advantages over representative baselines.
