Efficient compositional approaches for real-time robust direct visual odometry from RGB-D data
Efficient compositional approaches for real-time robust direct visual odometry from RGB-D data
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DOI：
10.1109/iros.2013.6696487
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发表时间：
2013-11
期刊：
影响因子：
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通讯作者：
S. Klose;Philipp Heise;Alois Knoll
中科院分区：
文献类型：
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作者：
S. Klose;Philipp Heise;Alois Knoll
在本文中，我们给出了不同的方法来计算帧到帧的运动估计移动RGB-D传感器，通过对齐两个图像，使用光度误差最小化的评估。这类算法最近被证明是非常准确和强大的，因此提供了一个有吸引力的解决方案，机器人自我运动估计和导航。我们展示了三种不同的对齐策略，即前向组合，逆组合和有效的二阶最小化方法，在一个一般的鲁棒估计框架。我们进一步展示了如何估计全局仿射照明的变化，一般提高了算法的性能。我们将我们的结果与最近发表的工作进行比较，这些工作被认为是该领域最先进的，并表明我们的解决方案通常更精确，并且可以在标准硬件上实时执行。
In this paper we give an evaluation of different methods for computing frame-to-frame motion estimates for a moving RGB-D sensor, by means of aligning two images using photometric error minimization. These kind of algorithms have recently shown to be very accurate and robust and therefore provide an attractive solution for robot ego-motion estimation and navigation. We demonstrate three different alignment strategies, namely the Forward-Compositional, the Inverse-Compositional and the Efficient Second-Order Minimization approach, in a general robust estimation framework. We further show how estimating global affine illumination changes, in general improves the performance of the algorithms. We compare our results with recently published work, considered as state-of-the art in this field, and show that our solutions are in general more precise and can perform in real-time on standard hardware.