人机协同控制无人机，智能补拍复杂区域的3D重建图像。
Stealthy Coverage Control for Human-enabled Real-Time 3D Reconstruction
- 人类主导识别需补拍区域，系统自动规划高效采样路径。
- 仿真显示人机协同比纯自动重建质量更高。
- 适合需要高精度3D建模的实地勘察场景。
本文提出一种新型半自主图像采样策略——隐蔽覆盖控制，用于人机协同的实时3D结构重建。当前任务的核心难题在于：准确重建3D模型所需的图像数量取决于目标场景的结构复杂度，但难以预先获知空间分布不均的复杂度。为此，我们利用人类灵活的推理与情境认知能力，设计了一种半自主系统，将识别需补充图像的区域及引导无人机前往这些区域的任务交由人工操作员完成。为此，我们首先提出一种在自主覆盖控制中反映人类意图的方法；随后，为避免手动操控与自主覆盖控制之间的冲突，开发了隐蔽覆盖控制，将高效图像采样与人类导航分离。基于Unity/ROS2的仿真结果表明，该半自主系统在重建模型质量上优于无人员干预的系统。
原文摘要 · Abstract (English)
In this paper, we propose a novel semi-autonomous image sampling strategy, called stealthy coverage control, for human-enabled 3D structure reconstruction. The present mission involves a fundamental problem: while the number of images required to accurately reconstruct a 3D model depends on the structural complexity of the target scene to be reconstructed, it is not realistic to assume prior knowledge of the spatially non-uniform structural complexity. We approach this issue by leveraging human flexible reasoning and situational recognition capabilities. Specifically, we design a semi-autonomous system that leaves identification of regions that need more images and navigation of the drones to such regions to a human operator. To this end, we first present a way to reflect the human intention in autonomous coverage control. Subsequently, in order to avoid operational conflicts between manual control and autonomous coverage control, we develop the stealthy coverage control that decouples the drone motion for efficient image sampling from navigation by the human. Simulation studies on a Unity/ROS2-based simulator demonstrate that the present semi-autonomous system outperforms the one without human interventions in the sense of the reconstructed model quality.
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