|Title||A Convex Relaxation for Approximate Global Optimization in Simultaneous Localization and Mapping|
|Publication Type||Conference Proceedings|
|Year of Conference||2015|
|Authors||Rosen DM, DuHadway C, Leonard JJ|
|Conference Name||International Conference on Robotics and Automation (ICRA)|
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.