Indoor Follow Me Drone.

With the availability of inexpensive and powerful drones, it is possible to let drones automatically follow a user for video taping. This can not only reduce cost, but also support video taping in situations where otherwise not possible (e.g., during private moments or at inconvenient locations like indoor rock climbing). While there have been many follow-me drones on the market for outdoors, which rely on GPS, enabling indoor follow-me function is more challenging due to the lack of an effective approach to track users in indoor environments. To this end, we develop a holistic system that lets a mobile phone carried by a user accurately track the drone's relative location and control it to maintain a specified distance and orientation for automatic video taping. We develop a series of techniques to (i) track a drone's location using acoustic signals with sub-centimeter errors even under strong propeller noise from the drone and complicated multipath in indoor environments, and (ii) solve practical challenges in applying model predictive control (MPC) framework to control the drone. The latter consists of developing measurement-based flight models, designing measurement techniques to provide feedback to the controller, and predicting the user's movement. We implement our system on AR Drone 2.0 and Samsung S7. The extensive evaluation shows that our drone can follow a user effectively and maintain a specified following distance and orientation within 2-3 cm and 1-3 degree errors, respectively. The videos taped by the drone during flight are smooth according to the jerk metric.