Abstract:

We present a novel framework for collaboration amongst a team of robots performing Pose Graph Optimization (PGO) that ad- dresses two important challenges for multi-robot SLAM: i) that of en- abling information exchange “on-demand” via Active Rendezvous without using a map or the robot’s location, and ii) that of rejecting outlying mea- surements. Our key insight is to exploit relative position data present in the communication channel between robots to improve groundtruth accu- racy 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 re- sults 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.