Markov Localization of an Indoor Quadcopter using Deep Learning

Localization is among one of the interesting subjects in robotics and can be spread from Unmanned Ground Vehicles (UGVs) to aerial ones. It is a point of interest for instance to localize robots in a warehouse or within an open area to define specific tasks. Unmanned Aerial Vehicles are also being used vastly indoors with GPSdenied environments. There are many localization methods recently being used in industry and research as such as Ultra-Wide Band (UWB), Bluetooth and (Global Positioning System) GPS. They have their own point of application in industry depending on their specifications. One of the best solutions is UWB with the least number of errors. In this thesis, we implemented a localization method based on Deep Learning. 16 patterns on the floor are used to make a specific map for localization. The proposed Deep Learning algorithm were able to detect each pattern correctly with 100% accuracy using majority voting for decision making in 3 seconds. The detection is performed real-time with the video feed of 30fps. Training and testing the network is done on Mobilenet which is based on Fast R-CNN deep learning architecture. All the processes are done on the quadcopter itself from navigation, control, and deep pattern detection using a single embedded computer. The quadcopter is equipped with a Raspberry Pi, Google Edge TPU embedded device with a flight controller in addition to a tracking and an RGB camera. The whole decision making of the patterns is performed via the embedded device connected to the Raspberry Pi in 30 fps and no pattern recognition process is employed on the ground computer. The drone odometry data is acquired via an Intel Realsense camera which provides IMU data to the drone. Only the codes for simple movements over the map have been sent to the drone from the ground station. Heading data is also provided by the tracking camera mounted on the quadcopter. Markov weights and the final decision weights have 100% confidence after each random path has been travelled over by the quadcopter. The drone was able to localize itself as a kidnapped robot, after flying over an average of two or maximum three patterns.