In this study, we address an exploration problem when constructing complete 3D models in an unknown environment using a Micro-Aerial Vehicle. Most previous exploration methods were based on the Next-Best-View (NBV) approaches, which iteratively determine the most informative view, that exposes the greatest unknown area from the current partial model. However, these approaches sometimes miss minor unreconstructed regions like holes or sparse surfaces (while these can be important features). Furthermore, because the NBV methods iterate the next-best path from a current partial view, they sometimes produce unnecessarily long trajectories by revisiting known regions. To address these problems, we propose a novel exploration algorithm that integrates coverage and inspection strategies. The suggested algorithm first computes a global plan to cover unexplored regions to complete the target model sequentially. It then plans local inspection paths that comprehensively scans local frontiers. This approach reduces the total exploration time and improves the completeness of the reconstructed models. We evaluate the proposed algorithm in comparison with other state-of-the-art approaches through simulated and real-world experiments. The results show that our algorithm outperforms the other approaches and in particular improves the completeness of surface coverage.

在这项研究中，我们解决了在未知环境中使用微型飞行器构建完整3D模型时的探索问题。以前的大多数探索方法都是基于次最佳视图（NBV）方法的，该方法反复确定信息最丰富的视图，从而从当前局部模型中暴露出最大的未知区域。但是，这些方法有时会错过较小的未重建区域，例如孔或稀疏表面（尽管这些可能是重要特征）。此外，由于NBV方法从当前局部视图中迭代出次佳路径，因此它们有时会通过重新访问已知区域而产生不必要的较长轨迹。为了解决这些问题，我们提出了一种新颖的探索算法，该算法整合了覆盖率和检查策略。建议的算法首先计算一个全局计划以覆盖未探索的区域，以按顺序完成目标模型。然后，它计划全面检查本地边界的本地检查路径。这种方法减少了总的探索时间并提高了重建模型的完整性。通过模拟和现实实验，我们与其他最新方法进行了比较，对提出的算法进行了评估。结果表明，我们的算法优于其他方法，特别是提高了表面覆盖的完整性。通过模拟和现实实验，我们与其他最新方法进行了比较，对提出的算法进行了评估。结果表明，我们的算法优于其他方法，特别是提高了表面覆盖的完整性。通过模拟和现实实验，我们与其他最新方法进行了比较，对提出的算法进行了评估。结果表明，我们的算法优于其他方法，特别是提高了表面覆盖的完整性。