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High-speed object matching and localization using gradient orientation features

机译：使用梯度定向功能进行高速对象匹配和定位

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In many robotics and automation applications, it is often required to detect a given object and determine its pose (position and orientation) from input images with high speed, high robustness to photometric changes, and high pose accuracy. We propose a new object matching method that improves efficiency over existing approaches by decomposing orientation and position estimation into two cascade steps. In the first step, an initial position and orientation is found by matching with Histogram of Oriented Gradients (HOG), reducing orientation search from 2D template matching to ID correlation matching. In the second step, a more precise orientation and position is computed by matching based on Dominant Orientation Template (DOT), using robust edge orientation features. The cascade combination of the HOG and DOT feature for high-speed and robust object matching is the key novelty of the proposed method. Experimental evaluation was performed with real-world single-object and multi-object inspection datasets, using software implementations on an Atom CPU platform. Our results show that the proposed method achieves significant speed improvement compared to an already accelerated template matching method at comparable accuracy performance.
机译：在许多机器人技术和自动化应用中，通常需要检测给定对象并从输入图像中以高速，高鲁棒性到光度变化以及高姿态精度来确定其姿态（位置和方向）。我们提出了一种新的对象匹配方法，该方法通过将方向和位置估计分解为两个层叠步骤来提高现有方法的效率。第一步，通过与定向梯度直方图（HOG）匹配来找到初始位置和方向，从而将方向搜索从2D模板匹配减少到ID相关匹配。在第二步中，使用鲁棒的边缘方向特征，根据基于主导方向模板（DOT）的匹配，计算出更精确的方向和位置。 HOG和DOT功能的级联组合用于高速和鲁棒的对象匹配是该方法的关键新颖之处。使用Atom CPU平台上的软件实现，对真实的单对象和多对象检查数据集进行了实验评估。我们的结果表明，与已经加速的模板匹配方法相比，该方法在相当的精度性能上实现了显着的速度改进。

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