Quality-guided UAV Surface Exploration for 3D Reconstruction

TL;DR

This paper presents a modular Next-Best-View planning framework for UAVs that uses reconstruction quality to guide exploration, improving 3D mapping efficiency and map quality in simulations.

Contribution

It introduces a novel adaptive view generation method based on environment uncertainty and quality objectives, enhancing exploration strategies for UAV-based 3D reconstruction.

Findings

Outperforms conventional NBV strategies in coverage and map quality

Adjusts exploration behavior according to user-defined quality requirements

Successfully validated through extensive realistic environment simulations

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. Finally, we validate our method via extensive simulations in realistic environments. 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.