疎な3Dトポロジカルグラフによるマイクロ航空機の経路計画
Sparse 3D Topological Graphs for Micro-Aerial Vehicle Planning
ノイズを含むセンサデータから3D環境のトポロジカル構造を表す疎なグラフを構築し、MAVの大域経路計画を高速化する手法を提案した。
著者: Helen Oleynikova, Zachary Taylor, Roland Siegwart, Juan Nieto
分類: cs.RO
原文アブストラクト
Micro-Aerial Vehicles (MAVs) have the advantage of moving freely in 3D space. However, creating compact and sparse map representations that can be efficiently used for planning for such robots is still an open problem. In this paper, we take maps built from noisy sensor data and construct a sparse graph containing topological information that can be used for 3D planning. We use a Euclidean Signed Distance Field, extract a 3D Generalized Voronoi Diagram (GVD), and obtain a thin skeleton diagram representing the topological structure of the environment. We then convert this skeleton diagram into a sparse graph, which we show is resistant to noise and changes in resolution. We demonstrate global planning over this graph, and the orders of magnitude speed-up it offers over other common planning methods. We validate our planning algorithm in real maps built onboard an MAV, using RGB-D sensing.
