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Robust Homing with Stereovision

机译:Stereovision强大的归位功能

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Visual Homing is a bioinspired approach to robot navigation which can be fast and uses few assumptions. However, visual homing in a cluttered and unstructured outdoor environment offers several challenges to homing methods that have been developed for primarily indoor environments. One issue is that any current image during homing may be tilted with respect to the home image. The second is that moving through a cluttered scene during homing may cause obstacles to interfere between the home scene and location and the current scene and location. In this paper, we introduce a robust method to improve a previous developed Homing with Stereo Vision (HSV) method for visual homing. HSV adds stereo information to the image information typically used in homing resulting in improved performance. The Robust Homing with Stereo Vision (RHSV) algorithm is modified to deal with current images taken at arbitrary pitch and roll values, and to handle homing and navigation through occluding obstacles. The results for several trials comparing HSV and RHSV are presented and the future direction of this work outlined.
机译:视觉归位是一种受生物启发的机器人导航方法,该方法可以快速且几乎不使用任何假设。但是,在杂乱无章的室外环境中进行视觉归位对主要为室内环境开发的归位方法提出了一些挑战。一个问题是,归位期间的任何当前图像都可能相对于原始图像倾斜。第二个原因是在归位期间穿越混乱的场景可能会导致障碍物干扰家庭场景和位置以及当前场景和位置。在本文中,我们介绍了一种鲁棒的方法,以改进以前开发的用于视觉归位的立体视觉(HSV)方法的归位。 HSV将立体声信息添加到通常用于归位的图像信息中,从而提高了性能。改进了具有立体视觉的鲁棒归位(RHSV)算法,以处理以任意俯仰和侧倾值拍摄的当前图像,并通过遮挡障碍物进行归位和导航。介绍了一些比较HSV和RHSV的试验结果,并概述了这项工作的未来方向。

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