We consider the problem of distributed pose graph optimization (PGO) that has important applications in multirobot simultaneous localization and mapping (SLAM). We propose the majorization minimization (MM) method for distributed PGO (<inline-formula><tex-math notation="LaTeX">$\mathsf {MM\text{--}PGO}$</tex-math></inline-formula>) that applies to a broad class of robust loss kernels. The <inline-formula><tex-math notation="LaTeX">$\mathsf {MM\text{--}PGO}$</tex-math></inline-formula> method is guaranteed to converge to first-order critical points under mild conditions. Furthermore, noting that the <inline-formula><tex-math notation="LaTeX">$\mathsf {MM\text{--}PGO}$</tex-math></inline-formula> 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 (<inline-formula><tex-math notation="LaTeX">$\mathsf {AMM\text{--}PGO}^*$</tex-math></inline-formula>) and without (<inline-formula><tex-math notation="LaTeX">$\mathsf {AMM\text{--}PGO}^{\#}$</tex-math></inline-formula>)—have faster convergence in contrast to the <inline-formula><tex-math notation="LaTeX">$\mathsf {MM\text{--}PGO}$</tex-math></inline-formula> method without sacrificing theoretical guarantees. In particular, the <inline-formula><tex-math notation="LaTeX">$\mathsf {AMM\text{--}PGO}^{\#}$</tex-math></inline-formula> 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 <inline-formula><tex-math notation="LaTeX">$\mathsf {AMM\text{--}PGO}^*$</tex-math></inline-formula> method using a master node to aggregate information from all the nodes. The efficacy of this work is validated through extensive applications to 2-D and 3-D 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.