用重建质量引导无人机探索，高效生成高精度3D地图。
Quality-guided UAV Surface Exploration for 3D Reconstruction
- 基于重建质量目标的模块化视角规划，自适应用户需求。
- 在真实环境中实现更高覆盖率与更优地图质量，路径更高效。
- 适合需要高质量3D建模的无人机测绘任务，如建筑检测。
自主机器人映射未知环境的原因多种多样，但在规划策略开发中常被忽视。快速信息获取与全面结构评估需求不同，需采用不同方法。本文提出一种新型模块化下一最佳视角（NBV）规划框架，针对空中机器人，显式以重建质量为目标指导探索规划。方法引入高效的视角生成与候选视角选择技术，能根据用户定义的质量要求自适应调整，充分挖掘环境在截断有符号距离场（TSDF）表示中的不确定性。由此做出更明智、更高效的探索决策，精准匹配预设目标。通过在真实环境中的大量仿真验证，结果表明该方法能根据用户目标灵活调整行为，在覆盖范围、最终3D地图质量及路径效率上均持续优于传统NBV策略。
原文摘要 · Abstract (English)
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.
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