Majorization Minimization Methods for Distributed Pose Graph Optimization

Understand

We consider the problem of distributed pose graph optimization (PGO) that has important applications in multi-robot simultaneous localization and mapping (SLAM).

• We propose the majorization minimization (MM) method for distributed PGO ($\mathsf{MM-PGO}$) that applies to a broad class of robust loss kernels.
• The $\mathsf{MM-PGO}$ method is guaranteed to converge to first-order critical points under mild conditions.
• Furthermore, noting that the $\mathsf{MM-PGO}$ method is reminiscent of proximal methods, we leverage Nesterov's method and adopt adaptive restarts to accelerate convergence.