The quadrotor helicopter is a unique flying vehicle which uses the thrust from four motors to provide hover flight capability. The uncoupled nature of the longitudinal and lateral axes and its ability to support large payloads with respect to its size make it an attractive vehicle for autonomous vehicle research. In this thesis, the quadrotor is modeled based on first principles and a proportional-derivative control method is applied for attitude stabilization and position control. A unique means of using an optic flow sensor for velocity and position estimation in an indoor setting is presented with flight results. Reliable hover flight and hallway following capabilities are exhibited in GPS-denied indoor flight using only onboard sensors. Attitude angles can be reliably estimated in the short run by integrating the angular rates from MEMS gyros, but noise on the signal leads to drift which renders the measurement unsuitable to attitude estimation. Typical methods of providing vector attitude corrections such as accelerometers and magnetometers have inherent weaknesses on hovering vehicles. Thus, an additional vector measurement is necessary to correct attitude readings for long-term flights. Two methods of using image processing to determine vanishing points in a hallway are demonstrated. The more promising of the two uses a Hough transform to detect lines in the image and forms a histogram of the intersections to detect likely vanishing point candidates. Once the vanishing point is detected, it acts as a vector measurement to correct attitude estimates on the quadrotor vehicle. Results using onboard vision to estimate heading are demonstrated on a test stand. Together, these capabilities improve the utility of the quadrotor platform for flight without the need of any external sensing capability.



College and Department

Ira A. Fulton College of Engineering and Technology; Mechanical Engineering



Date Submitted


Document Type





quadrotor, helicopter, attitude estimation, computer vision, vanishing point detection, optic flow sensor, MAGICC Lab