  In this paper, we deal with the problem of creating globally consistent pose
graphs in a centralized multi-robot SLAM framework. For each robot to act
autonomously, individual onboard pose estimates and maps are maintained, which
are then communicated to a central server to build an optimized global map.
However, inconsistencies between onboard and server estimates can occur due to
onboard odometry drift or failure. Furthermore, robots do not benefit from the
collaborative map if the server provides no feedback in a computationally
tractable and bandwidth-efficient manner. Motivated by this challenge, this
paper proposes a novel collaborative mapping framework to enable accurate
global mapping among robots and server. In particular, structural differences
between robot and server graphs are exploited at different spatial scales using
graph spectral analysis to generate necessary constraints for the individual
robot pose graphs. The proposed approach is thoroughly analyzed and validated
using several real-world multi-robot field deployments where we show
improvements of the onboard system up to 90%.
