This paper is about closing the low level control loop during Multirobot Simultaneous Localization and Map Building from an estimation-control theoretic viewpoint. We present a multi-vehicle control strategy that uses the state estimates generated from the SLAM algorithm as input to a multi-vehicle controller. Given the separability between optimal state estimation and regulation, we show that the tracking error does not influence the estimation performance of a fully observable EKF based multirobot SLAM implementation, and viceversa, that estimation errors do not undermine controller performance. Furthermore, both the controller and estimator are shown to be asymptotically stable. The feasibility of using this technique to close the perception-action loop during multirobot SLAM is validated with simulation results.