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

Thu Dec 7th 12:15 -- 12:20 PM @ Hall C

In this paper, we introduce a robust algorithm \textsl{TranSync} for the 1D translation synchronization problem, which aims to recover the global coordinates of a set of nodes from noisy relative measurements along a pre-defined 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 a deterministic noisy model, 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 an 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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