Scalable distributed collaborative tracking and mapping with Micro Aerial Vehicles

Abstract

This paper describes work on a distributed framework for collaborative multi-robot localisation and mapping with large teams of Micro Aerial Vehicles (MAVs). We demonstrate the benefits of running both image capture and frame-to-frame tracking on the same device while offloading the more computationally intensive aspects of map creation and optimization to an off-board computer. We show no impact on the accuracy of pose estimates of this distributed approach and indeed demonstrate a robustness to delay that improves localisation performance. The bandwidth requirements of our system are much lower than similar systems which enables us to accommodate larger teams of MAVs. In the results section we demonstrate the performance of our system in both simulated and real-world environments.