Quality-guided UAV Surface Exploration for 3D Reconstruction

Abstract-- Reasons for mapping an unknown environment with autonomous robots are wide-ranging, but in practice, they are often overlooked when developing planning strategies. Rapid information gathering and comprehensive structural assessment of buildings have different requirements and therefore necessitate distinct methodologies. In this paper, we propose a novel modular Next-Best-View (NBV) planning framework for aerial robots that explicitly uses a reconstruction quality objective to guide the exploration planning. In particular, our approach introduces new and efficient methods for view generation and selection of viewpoint candidates that are adaptive to the user-defined quality requirements, fully exploiting the uncertainty encoded in a Truncated Signed Distance field (TSDF) representation of the environment. This results in informed and efficient exploration decisions tailored towards the predetermined objective. We demonstrate that it successfully adjusts its behavior to the user goal while consistently outperforming conventional NBV strategies in terms of coverage, quality of the final 3D map and path efficiency. Autonomous exploration for 3D reconstruction is a fundamental task in robotics, with critical real-world applications such as infrastructure inspection, mapping, environmental monitoring, and search-and-rescue missions [1]. In these scenarios, Unmanned Aerial V ehicles (UA Vs) equipped with onboard visual and range sensors have proven essential for efficiently navigating complex environments and capturing aerial perspectives that enable comprehensive 3D reconstructions or a swift survey of an area.