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A Comparative Study of Two Vertical Road Modelling Techniques

机译:两种垂直道路建模技术的比较研究

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Binocular vision combined with stereo matching algorithms can be used in vehicles to gather data of the spatial proximity. To utilize this data we propose a new method for modeling the vertical road profile from a disparity map. This method is based on a region-growing technique, which iteratively performs a legist-squares fit of a B-spline curve to a region of selected points. We compare this technique to two variants of the ti-disparity method using either an envelope function or a planarity assumption. Our findings are that the proposed road-modeling technique outperforms both variants of the v-disparity technique, for which the planarity assumption is slightly better than the envelope version.
机译:结合立体匹配算法的双目视觉可用于车辆中以收集空间接近度的数据。为了利用这些数据,我们提出了一种新方法,用于根据视差图对垂直道路轮廓进行建模。此方法基于区域增长技术,该技术迭代地执行B样条曲线到所选点区域的Legist-squares拟合。我们将该技术与使用包络函数或平面度假设的ti视差方法的两个变体进行比较。我们的发现是,拟议的道路建模技术优于v视差技术的两种变体,对于这些变体,平面性假设略好于包络形式。

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