Object Identifier System for Autonomous UAV : A subsystem providing methods for detecting and descending to an object. The object is located in a specified area with a coverage algorithm.

Sammanfattning: Using UAVs in everyday life has been increasing in recent years. UAV is an agile vehicle and often comes integrated with a camera and sensors which makes it suitable for object detection and tracking. In this thesis, we present a subsystem with a limited hardware setup only consisting of an on-board computer and a camera that is mounted on a UAV. The subsystem provides techniques to maneuver, detect and descend to an object, all executed autonomously. The system is implemented in Robotic Operating System (ROS). The object detection is implemented as a convolutional neural network provided by TensorFlow Object Detection API. This thesis covers the necessary steps to adopt a pre-trained TensorFlow model to specific needs and compares three different TensorFlow models considering accuracy, frames per second and energy efficiency. Additionally, methodologies to cover a predefined area and position an object in relation to the camera is proposed. Experiments are executed both in a real-world and simulated environment and the results are promising for the implemented system.