  We present a novel framework for collaboration amongst a team of robots
performing Pose Graph Optimization (PGO) that addresses two important
challenges for multi-robot SLAM: i) that of enabling information exchange
"on-demand" via Active Rendezvous without using a map or the robot's location,
and ii) that of rejecting outlying measurements. Our key insight is to exploit
relative position data present in the communication channel between robots to
improve groundtruth accuracy of PGO. We develop an algorithmic and experimental
framework for integrating Channel State Information (CSI) with multi-robot PGO;
it is distributed, and applicable in low-lighting or featureless environments
where traditional sensors often fail. We present extensive experimental results
on actual robots and observe that using Active Rendezvous results in a 64%
reduction in ground truth pose error and that using CSI observations to aid
outlier rejection reduces ground truth pose error by 32%. These results show
the potential of integrating communication as a novel sensor for SLAM.
