| Περίληψη: | This thesis addresses the design and development of a system for the autonomous navigation of a quadcopter vehicle in an indoor, GPS-denied, industrial environment, potentially performing logistics operations. To achieve autonomous navigation, precise localization of the vehicle was essential and it was performed with the combination of two methods. The first made use of a visual Simultaneous Localization and Mapping (SLAM) algorithm, that relies on data of an onboard stereo camera. The second method relies on images from an onboard, bottom-facing monocular camera. Additionally, by employing five ultrasonic proximity sensors, the quadcopter is capable of performing efficient obstacle avoidance. The system has been integrated in the Robot Operating Software (ROS) and implemented in the simulation environment of the Virtual Robot Experimentation Platform (V-REP). Finally, a series of experiments has been conducted to evaluate the developed system’s performance and identify gaps and requirements for future research.
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