Department of Mechanical Engineering, National Chiao Tung University, Hsinchu 30010, Taiwan.
Department of Photonics, National Chiao Tung University, Hsinchu 30010, Taiwan.
Sensors (Basel). 2019 Mar 20;19(6):1380. doi: 10.3390/s19061380.
The capability of landing on previously unvisited areas is a fundamental challenge for an unmanned aerial vehicle (UAV). In this paper, we developed a vision-based motion estimation as an aid to improve landing performance. As an alternative to the common scenarios accompanying by external infrastructures or well-defined marker, the proposed hybrid framework can successfully land on a new area without any prior information about guiding marks. The implementation was based on the optical flow technique associated with a multi-scale strategy to overcome the decreasing field-of-view during the UAV descending. Compared with a commercial Global Positioning System (GPS) through a sequence of flight trials, the vision-aided scheme can effectively minimize the possible sensing error, thus, leading to a more accurate result. Moreover, this work has potential to integrate the fast-growing image learning process and yields more practical versatility for UAV applications in the future.
在无人机（UAV）领域，降落在之前未访问过的区域的能力是一个基本挑战。在本文中，我们开发了一种基于视觉的运动估计方法，以提高降落性能。与常见的需要外部基础设施或明确定义的标记的场景不同，所提出的混合框架可以在没有任何关于引导标记的先验信息的情况下成功降落在新区域。该实现基于与多尺度策略相关联的光流技术，以克服无人机下降过程中视场的减小。通过一系列飞行试验与商业全球定位系统（GPS）进行比较，视觉辅助方案可以有效地最小化可能的传感误差，从而得到更准确的结果。此外，这项工作有可能集成快速发展的图像学习过程，为未来无人机应用提供更实际的多功能性。
