位置推定不確かさを考慮した能動的フィールドマッピングのための情報経路計画
Informative Path Planning for Active Field Mapping under Localization Uncertainty
ロボットの自己位置推定の不確かさを明示的に考慮し、ガウス過程で環境場をモデル化して位置推定とマッピングを統合する新たな効用関数で経路を計画する手法を提案した。
著者: Marija Popovic, Teresa Vidal-Calleja, Jen Jen Chung, Juan Nieto, Roland Siegwart
分類: cs.RO
原文アブストラクト
Information gathering algorithms play a key role in unlocking the potential of robots for efficient data collection in a wide range of applications. However, most existing strategies neglect the fundamental problem of the robot pose uncertainty, which is an implicit requirement for creating robust, high-quality maps. To address this issue, we introduce an informative planning framework for active mapping that explicitly accounts for the pose uncertainty in both the mapping and planning tasks. Our strategy exploits a Gaussian Process (GP) model to capture a target environmental field given the uncertainty on its inputs. For planning, we formulate a new utility function that couples the localization and field mapping objectives in GP-based mapping scenarios in a principled way, without relying on any manually tuned parameters. Extensive simulations show that our approach outperforms existing strategies, with reductions in mean pose uncertainty and map error. We also present a proof of concept in an indoor temperature mapping scenario.
