Mutual Relative Localization in Heterogeneous Air-ground Robot Teams

Abstract

Air and ground robots with distinct sensing characteristics can be combined in a team to accomplish demanding tasks robustly. A key challenge in such heterogeneous systems is the design of a local positioning methodology where each robot estimates its location with respect to its neighbors. We propose a filtering-based relative localization algorithm for air-ground teams composed of vertical-take-off-and-landing drones and unmanned aerial vehicles. The team members interact through a sensing/communication mechanism relying on onboard units, which results in a mutual connection between the air and ground components. Exploiting the supplementary features of omnidirectional distance sensors and monocular cameras, the framework can function in all environments without fixed infrastructures. Various simulation and experiment results verify the competency of our approach.