RISE: An Incremental Trust-Region Method for Robust Online Sparse Least-Squares Estimation
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Leonard_RISE.pdf
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Author(s)
Kaess, Michael
Rosen, David Matthew
Leonard, John Joseph
Date Issued
September 2014
Journal
IEEE Transactions on Robotics
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Rosen, David M., Michael Kaess, and John J. Leonard. “RISE: An Incremental Trust-Region Method for Robust Online Sparse Least-Squares Estimation.” IEEE Trans. Robot. 30, no. 5 (October 2014): 1091–1108.
Version
Author's final manuscript
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Mechanical Engineering
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DOI of Published Version
https://doi.org/10.1109/tro.2014.2321852
