Sparse 3D Topological Graphs for Micro-Aerial Vehicle Planning

TL;DR

This paper introduces a method to create sparse topological graphs from noisy 3D maps for efficient MAV planning, significantly speeding up planning while maintaining robustness to noise and resolution changes.

Contribution

We develop a novel approach to extract sparse topological graphs from noisy 3D sensor data for MAV planning, improving efficiency and robustness over existing methods.

Findings

Speed-up of planning by orders of magnitude compared to traditional methods

Robustness of the graph to noise and resolution changes

Successful validation on real MAV onboard RGB-D maps

Abstract

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.