UAV地形モニタリングのためのマルチレゾリューション地図生成と情報量最大化経路計画
Multiresolution Mapping and Informative Path Planning for UAV-based Terrain Monitoring
ガウス過程の空間相関を事前分布に用いたベイズデータ融合で高度依存センサモデルを扱うマルチレゾリューション地図生成を提案し、グリッド探索と進化最適化で情報量の多い3次元飛行経路を計画する。農業バイオマス監視で従来の塗りつぶし飛行より最大45%誤差を削減した。
著者: Marija Popovic, Teresa Vidal-Calleja, Gregory Hitz, Inkyu Sa, Roland Siegwart, Juan Nieto
分類: cs.RO
原文アブストラクト
Unmanned aerial vehicles (UAVs) can offer timely and cost-effective delivery of high-quality sensing data. How- ever, deciding when and where to take measurements in complex environments remains an open challenge. To address this issue, we introduce a new multiresolution mapping approach for informative path planning in terrain monitoring using UAVs. Our strategy exploits the spatial correlation encoded in a Gaussian Process model as a prior for Bayesian data fusion with probabilistic sensors. This allows us to incorporate altitude-dependent sensor models for aerial imaging and perform constant-time measurement updates. The resulting maps are used to plan information-rich trajectories in continuous 3-D space through a combination of grid search and evolutionary optimization. We evaluate our framework on the application of agricultural biomass monitoring. Extensive simulations show that our planner performs better than existing methods, with mean error reductions of up to 45% compared to traditional "lawnmower" coverage. We demonstrate proof of concept using a multirotor to map color in different environments.
