Quality-guided UAV Surface Exploration for 3D Reconstruction

2025-11-25

AI Summary

Autonomous exploration for UAV-based 3D reconstruction in unknown environments remains challenging due to the lack of explicit integration of user-specified reconstruction quality objectives into view-planning decisions. Method: This paper proposes a modular, quality-driven Next-Best-View (NBV) planning framework. It explicitly embeds user-defined reconstruction quality targets into the NBV decision process, models environmental uncertainty via Truncated Signed Distance Fields (TSDF), and introduces an adaptive candidate viewpoint generation scheme coupled with a quality-weighted evaluation mechanism—enabling joint optimization of viewpoint sampling, assessment, and trajectory planning. Contribution/Results: Compared to conventional approaches, the framework significantly improves coverage completeness, 3D map accuracy, and path efficiency in simulation. It further supports dynamic adaptation to heterogeneous quality requirements, offering an interpretable and customizable paradigm for rapid information acquisition and structural assessment of built environments.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Search and Optimization: Sampling/Simulation-based Search
• Computer Vision: 3D Computer Vision

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

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.

Problem

• Developing modular NBV planning for UAVs using reconstruction quality objectives
• Generating adaptive viewpoints based on user-defined quality requirements
• Improving 3D mapping coverage, quality and path efficiency over conventional methods

Innovation

• NBV planning framework with reconstruction quality objective
• Adaptive view generation using TSDF uncertainty encoding
• Quality-guided UAV exploration outperforms conventional strategies