Translation Synchronization via Truncated Least Squares
Translation Synchronization via Truncated Least Squares
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发表时间：
2017-12
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通讯作者：
Xiangru Huang;Zhenxiao Liang;C. Bajaj;Qi-Xing Huang
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作者：
Xiangru Huang;Zhenxiao Liang;C. Bajaj;Qi-Xing Huang
在本文中，我们介绍了一个强大的算法，TranSync，为一维平移同步问题，其目的是恢复一组节点的全局坐标从噪声测量相对坐标沿着观察图。TranSync的基本思想是应用截断最小二乘法，其中每一步的解决方案用于逐渐修剪噪声测量。我们分析了确定性和随机噪声模型下的TranSync，证明了它的鲁棒性和稳定性。在合成数据集和真实的数据集上的实验结果表明，TranSync在效率和准确性方面都上级最先进的凸公式。
In this paper, we introduce a robust algorithm, TranSync, for the 1D translation synchronization problem, in which the aim is to recover the global coordinates of a set of nodes from noisy measurements of relative coordinates along an observation graph. The basic idea of TranSync is to apply truncated least squares, where the solution at each step is used to gradually prune out noisy measurements. We analyze TranSync under both deterministic and randomized noisy models, demonstrating its robustness and stability. Experimental results on synthetic and real datasets show that TranSync is superior to state-of-the-art convex formulations in terms of both efficiency and accuracy.