Autonomous landing systems for Micro Aerial Vehicles (MAV) have been proposed using various combinations of GPS-based, vision, and fiducial tag-based schemes. Landing is a critical activity that a MAV performs and poor resolution of GPS, degraded camera images, fiducial tags not meeting required specifications and environmental factors pose challenges. An ideal solution to MAV landing should account for these challenges and for operational challenges which could cause unplanned movements and landings. Most approaches do not attempt to solve this general problem but look at restricted sub-problems with at least one well-defined parameter. In this work, we propose a generalized end-to-end landing site detection system using a two-stage training mechanism, which makes no pre-assumption about the landing site. Experimental results show that we achieve comparable accuracy and outperform existing methods for the time required for landing.

翻译：微型航空飞行器(MAV)自动着陆系统(MAV)是利用全球定位系统、视像和标签法等各种组合提出来的。着陆是一个关键活动,MAV对全球定位系统、退化的相机图像、不符合所需规格的磁带标签和环境因素的分辨率不高,而且造成挑战。MAV着陆的理想解决办法应该考虑到这些挑战以及可能造成非计划性移动和着陆的业务挑战。大多数办法并不试图解决这个一般性问题,而是至少用一个明确界定的参数来研究有限的次级问题。在这个工作中,我们提议使用一个两阶段培训机制,普遍采用端到端着陆点探测系统,这个机制不会对着陆点进行预先吞并。实验结果显示,我们达到了可比的准确性,并超越了着陆所需时间的现有方法。