Sparse 3D Topological Graphs for Micro-Aerial Vehicle Planning
This addresses the challenge of efficient 3D planning for MAVs, which is incremental as it builds on existing mapping and planning techniques.
The paper tackles the problem of creating compact and sparse map representations for Micro-Aerial Vehicle (MAV) planning in 3D space by constructing a sparse graph from noisy sensor data, resulting in orders of magnitude speed-up over other planning methods.
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.