  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. The resulting accelerated MM
methods for distributed PGO -- both with a master node in the network
($\mathsf{AMM-PGO}^*$) and without ($\mathsf{AMM-PGO}^{\#}$) -- have faster
convergence in contrast to the $\mathsf{AMM-PGO}$ method without sacrificing
theoretical guarantees. In particular, the $\mathsf{AMM-PGO}^{\#}$ method,
which needs no master node and is fully decentralized, features a novel
adaptive restart scheme and has a rate of convergence comparable to that of the
$\mathsf{AMM-PGO}^*$ method using a master node to aggregate information from
all the other nodes. The efficacy of this work is validated through extensive
applications to 2D and 3D SLAM benchmark datasets and comprehensive comparisons
against existing state-of-the-art methods, indicating that our MM methods
converge faster and result in better solutions to distributed PGO.
