RISE: An Incremental Trust-Region Method for Robust Online Sparse Least-Squares Estimation

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“RISE: An Incremental Trust-Region Method for Robust Online Sparse Least-Squares Estimation” by D.M. Rosen, M. Kaess, and J.J. Leonard. IEEE Trans. on Robotics, TRO, 2014. To appear.

Abstract

Many point estimation problems in robotics, computer vision and machine learning can be formulated as instances of the general problem of minimizing a sparse nonlinear sum-of-squares objective function. For inference problems of this type, each input datum gives rise to a summand in the objective function, and therefore performing online inference corresponds to solving a sequence of sparse nonlinear least-squares minimization problems in which additional summands are added to the objective function over time. In this paper we present Robust Incremental least-Squares Estimation (RISE), an incrementalized version of the Powell's Dog-Leg numerical optimization method suitable for use in online sequential sparse least-squares minimization. As a trust-region method, RISE is naturally robust to objective function nonlinearity and numerical ill-conditioning, and is provably globally convergent for a broad class of inferential cost functions (twice-continuously differentiable functions with bounded sublevel sets). Consequently, RISE maintains the speed of current state-of-the-art online sparse least-squares methods while providing superior reliability.

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BibTeX entry:

@article{Rosen14tro,
   author = {D.M. Rosen and M. Kaess and J.J. Leonard},
   title = {{RISE}: An Incremental Trust-Region Method for Robust Online
	Sparse Least-Squares Estimation},
   journal = {IEEE Trans. on Robotics, TRO},
   year = {2014},
   note = {To appear}
}
Last updated: Aug 14, 2014