Autonomous multi-floor indoor navigation with a computationally constrained micro aerial vehicle

We are interested in the problem of surveilling and exploring environments that include both indoor and outdoor settings. Aerial vehicles offer mobility and perspective advantages over ground platforms and micro aerial vehicles (MAVs) are particularly applicable to buildings with multiple floors where stairwells can be an obstacle to ground vehicles. A challenge when operating in indoor environments is the lack of an external source of localization such as GPS. For these reasons, in this work we focus on autonomous navigation in buildings with multiple floors without requiring an external source of localization or prior knowledge of the environment. To ensure that the robot is fully autonomous, we require all computation to occur on the robot without need for external infrastructure, communication, or human interaction beyond high-level commands. Therefore, we pursue a system design and methodology capable of autonomous navigation with real-time performance on a mobile processor using only onboard sensors (Fig. 1); where in this work autonomous navigation considers multi-floor mapping with loop closure, localization, planning, and control.