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Real time obstacle detection in stereovision on non flat road geometry through 'v-disparity' representation

机译:通过“ v-视差”表示在非平坦道路几何体上的立体视觉中实时障碍物检测

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Presents a road obstacle detection method able to cope with uphill and downhill gradients and dynamic pitching of the vehicle. Our approach is based on the construction and investigation of the "v-disparity" image which provides a good representation of the geometric content of the road scene. The advantage of this image is that it provides semi-global matching and is able to perform robust obstacle detection even in the case of partial occlusion or errors committed during the matching process. Furthermore, this detection is performed without any explicit extraction of coherent structures. This paper explains the construction of the "v-disparity" image, its main properties, and the obstacle detection method. The longitudinal profile of the road is estimated and the objects located above the road surface are then extracted as potential obstacles; subsequently, the accurate detection of road obstacles, in particular the position of tyre-road contact points is computed in a precise manner. The whole process is performed at frame rate with a current-day PC. Our experimental findings and comparisons with the results obtained using a flat geometry hypothesis show the benefits of our approach.
机译:提出了一种道路障碍物检测方法,该方法能够应对上坡和下坡的坡度以及车辆的动态俯仰。我们的方法基于“ v视差”图像的构建和研究,该图像可以很好地表示道路场景的几何内容。该图像的优势在于,它提供了半全局匹配,即使在匹配过程中出现部分遮挡或错误的情况下,也能够执行鲁棒的障碍物检测。此外,执行此检测时不会显式提取相干结构。本文介绍了“ v视差”图像的构造,其主要属性以及障碍物检测方法。估算道路的纵向轮廓,然后提取位于路面上方的物体作为潜在障碍;随后,以精确的方式计算对道路障碍物,特别是轮胎-道路接触点的位置的精确检测。整个过程是使用当前PC以帧速率执行的。我们的实验结果以及与使用平面几何假设获得的结果的比较表明了我们方法的好处。

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