Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Fresh data on Robotics are presented i n a new report. According to news reporting from Cambridge, Massachusetts, by Ne wsRx journalists, research stated, "We propose fast and communicationefficient optimization algorithms for multirobot rotation averaging and translation estima tion problems that arise from collaborative simultaneous localization and mappin g (SLAM), structure-from-motion (SfM), and camera network localization applicati ons. Our methods are based on theoretical relations between the Hessians of the underlying Riemannian optimization problems and the Laplacians of suitably weigh ted graphs." Financial support for this research came from ARL DCIST. The news correspondents obtained a quote from the research from the Massachusett s Institute of Technology, "We leverage these results to design a collaborative solver in which robots coordinate with a central server to perform approximate s econd-order optimization, by solving a Laplacian system at each iteration. Cruci ally, our algorithms permit robots to employ spectral sparsification to sparsify intermediate dense matrices before communication, and hence provide a mechanism to tradeoff accuracy with communication efficiency with provable guarantees. We perform rigorous theoretical analysis of our methods and prove that they enjoy (local) linear rate of convergence. Furthermore, we show that our methods can be combined with graduated nonconvexity to achieve outlier-robust estimation."
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