Autori
Luca Carlone, Giuseppe Carlo Calafiore, Carlo Tommolillo and Frank Dallaert
Abstract
Pose Graph Optimization (PGO) is the problem of estimating a set of poses from pairwise relative measurements. PGO is a nonconvex problem, and currently no known technique can guarantee the computation of a global optimal solution. We show that Lagrangian duality allows computing a globally optimal solution under conditions that are satisfied in most robotics applications and in the majority of tests under very large noise regimes. Furthermore, it enables to verify if a given estimate (e.g., computed using iterative solvers) is globally optimal.
