Global optimization for 2D SLAM problem

Abstract

A globally optimal approach is proposed in this work for map-joining SLAM problem. Traditionally local optimization based approaches are adapted for SLAM problem but due to highly non-convex nature of the SLAM problem, they are susceptible to local minima. In this work, we have exploited the theoretical limit on the number of local minima. The proposed approach is not dependent upon the good initial guess whereas existing approaches in SLAM literature requires a good starting point for convergence to the basin of global minima. Simulation and real dataset results are provided to validate the robustness of the approach to converge to global minima. This chapter provides the robotics community to look into the SLAMproblem with global optimization approach by guarantying the global optimal solution in a least square cost function particularly when covariance matrices are defined as spherical.