Landing safety is a challenge heavily engaging the research community recently, due to the increasing interest in applications availed by aerial vehicles. In this paper, we propose a landing safety pipeline based on state of the art object detectors and OctoMap. First, a point cloud of surface obstacles is generated, which is then inserted in an OctoMap. The unoccupied areas are identified, thus resulting to a list of safe landing points. Due to the low inference time achieved by state of the art object detectors and the efficient point cloud manipulation using OctoMap, it is feasible for our approach to deploy on low-weight embedded systems. The proposed pipeline has been evaluated in many simulation scenarios, varying in people density, number, and movement. Simulations were executed with an Nvidia Jetson Nano in the loop to confirm the pipeline's performance and robustness in a low computing power hardware. The experiments yielded promising results with a 95% success rate.

翻译：着陆安全是近年来因空中交通工具应用日益增长而引发研究界关注的重要挑战。本文提出了一种基于先进目标检测器与OctoMap的着陆安全流程：首先生成地表障碍物点云并插入OctoMap，通过识别非占据区域获得安全着陆点列表。得益于先进目标检测器的低推理时间及OctoMap对点云的高效处理，该方法可部署于低重量嵌入式系统。我们已在多种人员密度、数量及运动模式的仿真场景中评估该流程，并通过Nvidia Jetson Nano硬件在环仿真验证其在低算力硬件上的性能与鲁棒性。实验结果显示95%的成功率，成果具有应用前景。