Simultaneous localization and mapping with the AR.Drone

The small size of micro aerial vehicles (MAVs) allows a wide range of robotic applications, such as surveillance, inspection and search & rescue. In order to operate autonomously, the robot requires the ability to known its position and movement in the environment. Since no assumptions can be made about the environment, the robot has to learn from its environment. Simultaneous Localization and Mapping (SLAM) using aerial vehicles is an active research area in robotics. However, current approaches use algorithms that are computationally expensive and cannot be applied for real-time navigation problems. Furthermore, most researchers rely on expensive aerial vehicles with advanced sensors. This thesis presents a real-time SLAM approach for affordable MAVs with a down-looking camera. Focusing on real-time methods and affordable MAVs increases the employability of aerial vehicles in real world situations. The approach has been validated with the AR.Drone quadrotor helicopter, which was the standard platform for the International Micro Air Vehicle competition. The development is partly based on simulation, which requires both a realistic sensor and motion model. The AR.Drone simulation model is described and validated. Furthermore, this thesis describes how a visual map of the environment can be made. This visual map consists of a texture map and a feature map. The texture map is used for human navigation and the feature map is used by the AR.Drone to localize itself. A localization method is presented. It uses a novel approach to robustly recover the translation and rotation between a camera frame and the map. An experimental method to create an elevation map with a single airborne ultrasound sensor is presented. This elevation map is combined with the texture map and visualized in real-time. Experiments have validated that the presented methods work in a variety of environments. One of the experiments demonstrates how well the localization works for circumstances encountered during the IMAV competition. Furthermore, the impact of the camera resolution and various pose recovery approaches are investigated. Parts of this thesis have been published in: N. Dijkshoorn and A. Visser, ”Integrating Sensor and Motion Models to Localize an Autonomous AR. Drone”, International Journal of Micro Air Vehicles, volume 3, pp. 183-200, 2011 A. Visser, N. Dijkshoorn, M. van der Veen, R. Jurriaans, ”Closing the gap between simulation and reality in the sensor and motion models of an autonomous AR.Drone”, Proceedings of the International Micro Air Vehicle Conference and Flight Competition (IMAV11), 2011. Nominated for best paper award