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

TitleAn Incremental Trust-Region Method for Robust Online Sparse Least-Squares Estimation
Publication TypeConference Paper
Year of Publication2012
AuthorsRosen DM, Kaess M, Leonard JJ
Conference Nameicra
Date Publishedmay
Conference LocationSt Paul, MN
Abstract

Many online inference problems in computer vision and robotics are characterized by probability distributions whose factor graph representations are sparse and whose factors are all Gaussian functions of error residuals Under these conditions, maximum likelihood estimation corresponds to solving a sequence of sparse 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 trust-region method suitable for use in online sparse least-squares minimization As a trust-region method, Powell’s Dog-Leg enjoys excellent global convergence properties, and is known to be considerably faster than both Gauss-Newton and Levenberg-Marquardt when applied to sparse least-squares problems Consequently, RISE maintains the speed of current state-of-the-art incremental sparse least-squares methods while providing superior robustness to objective function nonlinearities

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