Huang Xiangru, Bajaj Chandrajit, Liang Zhenxiao, Huang Qixing
The University of Texas at Austin, 2317 Speedway, Austin, 78712.
Tsinghua University, Beijing, China, 100084.
Adv Neural Inf Process Syst. 2017 Dec;30:1459-1468.
In this paper, we introduce a robust algorithm, , 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.
在本文中，我们针对一维平移同步问题引入了一种稳健算法TranSync，其目的是从沿观测图的相对坐标的噪声测量中恢复一组节点的全局坐标。TranSync的基本思想是应用截断最小二乘法，其中每一步的解用于逐步剔除噪声测量。我们在确定性和随机噪声模型下对TranSync进行了分析，证明了其稳健性和稳定性。在合成数据集和真实数据集上的实验结果表明，TranSync在效率和准确性方面均优于当前最先进的凸规划方法。
