  Distributed pose graph optimization (DPGO) is one of the fundamental
techniques of swarm robotics. Currently, the sub-problems of DPGO are built on
the native poses. Our validation proves that this approach may introduce an
imbalance in the sizes of the sub-problems in real-world scenarios, which
affects the speed of DPGO optimization, and potentially increases communication
requirements. In addition, the coherence of the estimated poses is not
guaranteed when the robots in the swarm fail, or partial robots are
disconnected. In this paper, we propose BDPGO, a balanced distributed pose
graph optimization framework using the idea of decoupling the robot poses and
DPGO. BDPGO re-distributes the poses in the pose graph to the robot swarm in a
balanced way by introducing a two-stage graph partitioning method to build
balanced subproblems. Our validation demonstrates that BDPGO significantly
improves the optimization speed without changing the specific algorithm of DPGO
in realistic datasets. What's more, we also validate that BDPGO is robust to
robot failure, changes in the wireless network. BDPGO has capable of keeps the
coherence of the estimated poses in these situations. The framework also has
the potential to be applied to other collaborative simultaneous localization
and mapping (CSLAM) problems involved in distributedly solving the factor
graph.
