  Due to the highly non-convex nature of large-scale robust parameter
estimation, avoiding poor local minima is challenging in real-world
applications where input data is contaminated by a large or unknown fraction of
outliers. In this paper, we introduce a novel solver for robust estimation that
possesses a strong ability to escape poor local minima. Our algorithm is built
upon the class of traditional graduated optimization techniques, which are
considered state-of-the-art local methods to solve problems having many poor
minima. The novelty of our work lies in the introduction of an adaptive kernel
(or residual) scaling scheme, which allows us to achieve faster convergence
rates. Like other existing methods that aim to return good local minima for
robust estimation tasks, our method relaxes the original robust problem but
adapts a filter framework from non-linear constrained optimization to
automatically choose the level of relaxation. Experimental results on real
large-scale datasets such as bundle adjustment instances demonstrate that our
proposed method achieves competitive results.
