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We present a novel framework for collaboration amongst a team of robotsperforming Pose Graph Optimization (PGO) that addresses two importantchallenges 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 exploitrelative position data present in the communication channel between robots toimprove groundtruth accuracy of PGO. We develop an algorithmic and experimentalframework for integrating Channel State Information (CSI) with multi-robot PGO;it is distributed, and applicable in low-lighting or featureless environmentswhere traditional sensors often fail. We present extensive experimental resultson actual robots and observe that using Active Rendezvous results in a 64%reduction in ground truth pose error and that using CSI observations to aidoutlier rejection reduces ground truth pose error by 32%. These results showthe potential of integrating communication as a novel sensor for SLAM.

Active Rendezvous for Multi-Robot Pose Graph Optimization using Sensing over Wi-Fi