RISE: An Incremental Trust-Region Method for Robust Online Sparse Least-Squares Estimation
Author(s)Kaess, Michael; Rosen, David Matthew; Leonard, John Joseph
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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.
DepartmentMassachusetts 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
IEEE Transactions on Robotics
Institute of Electrical and Electronics Engineers (IEEE)
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.
Author's final manuscript