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In this work we present initial results of a system that combines wearable technology and monocular simultaneous localisation and mapping (SLAM) for remote controlling of a low-cost micro aerial vehicle (MAV) that flies beyond the visual line-of-sight. To this purpose, as a first step, we use a state-of-the-art visual SLAM system, called ORB-SLAM, to create a 3D map of the scene. The visual data feeding...
In the last couple of years a novel visual simultaneous localisation and mapping (SLAM) system, based on visual features, has emerged as one of the best, if not the best, systems for estimating the 6D camera pose whilst building a 3D map of the observed scene. This method is called ORB-SLAM and one of its key ideas is to use the same visual descriptor, a binary descriptor called ORB, for all the visual...
Building on our previous work [1], in this paper we demonstrate how it is possible to improve flight control of a MAV that experiences aerodynamic disturbances caused by objects on its path. Predictions based on low resolution depth images taken at a distance are incorporated into the flight control loop on the throttle channel as this is adjusted to target undisrupted level flight. We demonstrate...
Many applications of Micro Air Vehicles (MAVs) require them to operate in cluttered environments, flying in constrained spaces and close to obstacles. Such obstacles affect the airflow around the MAV and can thereby affect its flight characteristics. We describe a system for predicting these effects at a distance, using depth images obtained from an RGB-D sensor. Predictions are based on learning...
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