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Optical-Flow based Self-Supervised Learning of Obstacle Appearance applied to MAV Landing

机译:基于光流的障碍物外观自监督学习   适用于maV登陆

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摘要

Monocular optical flow has been widely used to detect obstacles in Micro AirVehicles (MAVs) during visual navigation. However, this approach requiressignificant movement, which reduces the efficiency of navigation and may evenintroduce risks in narrow spaces. In this paper, we introduce a novel setup ofself-supervised learning (SSL), in which optical flow cues serve as a scaffoldto learn the visual appearance of obstacles in the environment. We apply it toa landing task, in which initially 'surface roughness' is estimated from theoptical flow field in order to detect obstacles. Subsequently, a linearregression function is learned that maps appearance features represented bytexton distributions to the roughness estimate. After learning, the MAV candetect obstacles by just analyzing a still image. This allows the MAV to searchfor a landing spot without moving. We first demonstrate this principle to workwith offline tests involving images captured from an on-board camera, and thendemonstrate the principle in flight. Although surface roughness is a propertyof the entire flow field in the global image, the appearance learning evenallows for the pixel-wise segmentation of obstacles.
机译:单目光流已广泛用于在视觉导航过程中检测微型飞机(MAV)中的障碍物。但是,这种方法需要大量移动,这会降低导航效率,甚至可能在狭窄空间内引发风险。在本文中,我们介绍了一种新颖的自我监督学习(SSL)设置,其中光流线索充当了学习环境中障碍物视觉外观的支架。我们将其应用于着陆任务,其中最初从光流场估计“表面粗糙度”以检测障碍物。随后,学习了线性回归函数,该函数将由texton分布表示的外观特征映射到粗糙度估计。学习后,MAV只需分析静止图像即可检测到障碍物。这使MAV无需移动即可搜索着陆点。我们首先演示此原理,以与涉及从机载摄像头捕获的图像的离线测试一起工作,然后演示飞行中的原理。尽管表面粗糙度是全局图像中整个流场的属性,但是外观学习甚至可以对障碍物进行像素分割。

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