Surface-driven Next-Best-View planning for exploration of large-scale 3D environments

Abstract

Abstract In this paper, we propose a novel cluster-based informative path planning algorithm to simultaneously explore and inspect a large-scale unknown environment with an Unmanned Aerial Vehicle (UAV). Most of the existing methods address the surface inspection problem as a volume exploration problem, and consider that the surface has been scanned when the corresponding volume has been covered. Unfortunately, this approach may lead to inaccurate 3D models of the environment, and the UAV may not achieve global coverage. To overcome these critical limitations, we introduce a 3D reconstruction method based on TSDF (Truncated Signed Distance Function) mapping, which leverages the surfaces present in the environment to generate an informative exploration path for the UAV. A Probabilistic Roadmap planner, used to solve a TSP (Travelling Salesman Problem) over clusters of viewpoint configurations, ensures that the resulting 3D model is accurate and complete. Two challenging structures (a power plant and the Statue of Liberty) have been chosen to conduct realistic numerical experiments with a quadrotor UAV. Our results provide evidence that the proposed method is effective and robust.