无人机3D全覆盖拍摄，轨迹与镜头协同优化
Jointly-optimized Trajectory Generation and Camera Control for 3D Coverage Planning
- 轨迹与相机控制联合优化，滚动规划提升覆盖率
- 结合光线追踪模拟视野，生成精准预览覆盖路径
- 适用于无人机3D建模、巡检等需全视角覆盖场景
本文提出一种轨迹生成与相机控制联合优化的方法，使自主飞行器（如在三维环境中运行的无人机）能够规划并执行最大化覆盖目标三维物体表面的航迹。具体而言，无人机的动力学和相机控制输入在滚动规划时域内联合优化，实现对目标物体的完整三维覆盖。所提控制器将光线追踪融入规划过程，模拟光路传播，从而通过无人机相机确定可见区域，实现精确的前瞻覆盖轨迹生成。覆盖规划问题被建模为滚动有限时域最优控制问题，并采用混合整数规划求解。大量真实世界与合成环境实验验证了该方法的有效性。
原文摘要 · Abstract (English)
This work proposes a jointly optimized trajectory generation and camera control approach, enabling an autonomous agent, such as an unmanned aerial vehicle (UAV) operating in 3D environments, to plan and execute coverage trajectories that maximally cover the surface area of a 3D object of interest. Specifically, the UAV's kinematic and camera control inputs are jointly optimized over a rolling planning horizon to achieve complete 3D coverage of the object. The proposed controller incorporates ray-tracing into the planning process to simulate the propagation of light rays, thereby determining the visible parts of the object through the UAV's camera. This integration enables the generation of precise look-ahead coverage trajectories. The coverage planning problem is formulated as a rolling finite-horizon optimal control problem and solved using mixed-integer programming techniques. Extensive real-world and synthetic experiments validate the performance of the proposed approach.
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