Applied Artificial Intelligence Group (GIAA), Carlos III University of Madrid, 28270 Madrid, Spain.
Sensors (Basel). 2022 May 10;22(10):3625. doi: 10.3390/s22103625.
New applications are continuously appearing with drones as protagonists, but all of them share an essential critical maneuver-landing. New application requirements have led the study of novel landing strategies, in which vision systems have played and continue to play a key role. Generally, the new applications use the control and navigation systems embedded in the aircraft. However, the internal dynamics of these systems, initially focused on other tasks such as the smoothing trajectories between different waypoints, can trigger undesired behaviors. In this paper, we propose a landing system based on monocular vision and navigation information to estimate the helipad global position. In addition, the global estimation system includes a position error correction module by cylinder space transformation and a filtering system with a sliding window. To conclude, the landing system is evaluated with three quality metrics, showing how the proposed correction system together with stationary filtering improves the raw landing system.
新型应用不断涌现,以无人机为主角,但它们都有一个必不可少的关键动作——着陆。新型应用需求促使人们研究新型着陆策略,其中视觉系统发挥了关键作用,并将继续发挥关键作用。一般来说,新应用使用嵌入在飞机中的控制和导航系统。然而,这些系统的内部动力学最初专注于其他任务,例如在不同航点之间平滑轨迹,可能会引发不期望的行为。在本文中,我们提出了一种基于单目视觉和导航信息的着陆系统,用于估计停机坪的全局位置。此外,全局估计系统包括一个通过圆柱空间变换进行位置误差校正的模块和一个带有滑动窗口的滤波系统。最后,使用三个质量指标对着陆系统进行评估,展示了所提出的校正系统与固定滤波相结合如何提高原始着陆系统的性能。