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Visual Servoing With Photometric Gaussian Mixtures as Dense Features

机译：使用光度高斯混合物作为密集特征的视觉伺服

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摘要

The direct use of the entire photometric image information as dense features for visual servoing brings several advantages. First, it does not require any feature detection, matching, or tracking process. Thanks to the redundancy of visual information, the precision at convergence is highly accurate. However, the corresponding highly nonlinear cost function reduces the convergence domain. In this paper, we propose visual servoing based on the analytical formulation of Gaussian mixtures to enlarge the convergence domain. Pixels are represented by two-dimensional Gaussian functions that denote a "power of attraction." In addition to the control of the camera velocities during the servoing, we also optimize the Gaussian spreads allowing the camera to precisely converge to a desired pose even from a far initial one. Simulations show that our approach outperforms the state of the art and real experiments show the effectiveness, robustness, and accuracy of our approach.
机译：将整个光度图像信息直接用作视觉伺服的密集特征具有许多优点。首先，它不需要任何特征检测，匹配或跟踪过程。由于视觉信息的冗余，收敛时的精度非常高。但是，相应的高度非线性成本函数会减小收敛域。在本文中，我们提出基于高斯混合物解析公式的视觉伺服，以扩大收敛域。像素由表示“吸引力”的二维高斯函数表示。除了在伺服过程中控制相机速度外，我们还优化了高斯分布，使相机甚至可以精确收敛到所需姿势。从最初的那个开始仿真表明，我们的方法优于现有技术，而实际实验则表明了该方法的有效性，鲁棒性和准确性。

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