A Convex Relaxation for Approximate Global Optimization in Simultaneous Localization and Mapping

TitleA Convex Relaxation for Approximate Global Optimization in Simultaneous Localization and Mapping
Publication TypeConference Proceedings
Year of Conference2015
AuthorsRosen DM, DuHadway C, Leonard JJ
Conference NameInternational Conference on Robotics and Automation (ICRA)
Date Published05/2015
Abstract

Modern approaches to simultaneous localization and mapping (SLAM) formulate the inference problem as a highdimensional but sparse nonconvex M-estimation, and then apply general first- or second-order smooth optimization methods to recover a local minimizer of the objective function. The performance of any such approach depends crucially upon initializing the optimization algorithm near a good solution for the inference problem, a condition that is often difficult or impossible to guarantee in practice. To address this limitation, in this paper we present a formulation of the SLAM M-estimation with the property that, by expanding the feasible set of the estimation program, we obtain a convex relaxation whose solution approximates the globally optimal solution of the SLAM inference problem and can be recovered using a smooth optimization method initialized at any feasible point. Our formulation thus provides a means to obtain a high-quality solution to the SLAM problem without requiring high-quality initialization.

PDF: