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Translation Synchronization via Truncated Least Squares
Xiangru Huang · Zhenxiao Liang · Chandrajit Bajaj · Qixing Huang

Wed Dec 06 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #47 #None

In this paper, we introduce a robust algorithm, \textsl{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.

Author Information

Xiangru Huang (University of Texas at Austin)

I'm currently a PhD student in University of Texas at Austin. My advisor is Qixing Huang.

Zhenxiao Liang (Tsinghua University)
Chandrajit Bajaj (The University of Texas at Austin)
Qixing Huang (The University of Texas at Austin)

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