Abstract
Recently, the drone-related applications are more and more popular such as photography, mapping, real estate and disaster response. In this thesis, the indoor positioning and tracking system for drones is proposed. It includes three parts: positioning component locates position of specified wireless device; tracking component tracks the selected object by pattern recognition; and movement decision component decides the drone’s movement. We have implemented the modified cell of origin (CoO) indoor positioning algorithm with the wireless device, which is the one limits its communication range and processes some calculation with the built-in microcontroller. For the tracking component, we have implemented tracking-learning-detection (TLD) algorithm. TLD is a computer vision algorithm that tracks unknown objects and recognizes object patterns. If tracking is successful, the margin of error will be less than 10 cm. The movement decision component computes feedback from positioning and tracking components to generate the drone’s movement. It positions objects to the center of image and directs the drone to the location commanded by the user. Finally, we propose a system which combines the CoO indoor positioning algorithm, the TLD algorithm, and the movement decisions for drones. The system locates the position of drone with the wireless device, tracks objects through drone’s camera, aims drone’s camera to the object, and flies the drone to desired locations. The test results show 2 m accuracy of positioning, and improved successful tracking rate of 33% higher than other tracking algorithms as control groups. The positioning algorithm contributes to the environment model unnecessary and the scalable coverage of positioning. Moreover, the wireless device’s size of 3 cm * 2 cm * 2 cm makes the system portable and the power consumption of 49.9 mW under 3.0 V operation voltage on a 850 mAh battery keeps the wireless device working over 2 days.