|Title||RISE: An Incremental Trust-Region Method for Robust Online Sparse Least-Squares Estimation|
|Publication Type||Journal Article|
|Year of Publication||2014|
|Authors||Rosen DM, Kaess M, Leonard JJ|
|Journal||IEEE Transactions on Robotics|
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-squaresobjective 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-SquaresEstimation (RISE), an incrementalized version of the Powell's Dog-Leg numerical optimizationmethod suitable for use in online sequential sparse least-squares minimization. As a trust-regionmethod, 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.