提升无人机的自主着陆能力对于提高无人机的作业效率和野外生存能力具有重要意义.本文提出了一种基于机载视频的无人机降落区域自动检测方法,目的是在缺乏场景先验知识的情况下,提高无人机的自主避障着陆能力.本文将多视图几何约束方法的深度学习网络融入到视觉同步定位与制图(Simultaneous localization and mapping,SLAM)算法中,旨在构建场景的三维地图,同时主动判别潜在障碍物.随后,提出了一种顾及降落区域面积及平坦度等因素的降落区域检测算法,通过体素网格地图的空间分析方式,判别出无人机着陆区域.在不同类别场景中分别进行实验,结果表明了提出方法的准确性.
Unmanned Aerial Vehicle Landing Area Detection Based on Onboard Video
Improving the autonomous landing capability of unmanned aerial vehicles(UAVs)holds significant importance in enhancing their operational efficiency and survival ability in the field.This paper presents a novel approach utilizing onboard video for automatic detection of UAV landing zones,aiming to enhance the UAV's autonomous obstacle avoidance and landing capabilities in the absence of prior scene knowledge.We integrate a deep learning network incorporating multi-view geometric constraint methods into the simultaneous localization and mapping(SLAM)algorithm,aiming to construct a three-dimensional map of the scene while actively identifying potential obstacles.Subsequently,we propose a landing area detection algorithm that takes into account factors such as landing area and flatness.By conducting spatial analysis on voxel grid maps,we can identify the landing area of UAVs.This algorithm utilizes spatial analysis on a voxel grid map to identify the suitable landing area for the UAV.Experimental evaluation is conducted in various scenarios,demonstrating the accuracy of the proposed approach.
unmanned aerial vehiclelanding area detectiondynamic sceneobject detectionsimultaneous localization and mapping
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