The goal of my MSc thesis is to contribute to the department's quadrocopter project with designing a position estimation unit which can be used later when implementing the altitude control of the helicopter.
Position control strongly depends on orientation control, and so does position estimation on orientation estimation, therefore the first major part of my work is covering the problem of orientation estimation.
The thesis gives a detailed summary of the most commonly used methods in position estimation and explains the need for sensor fusion to combine multiple methods for optimum accuracy. As a widely used method of sensor fusion, the algorithm of Kalman filtering is summarized.
The main chapter of the work presents the method developed for position estimation using GPS, inertial navigation and barometric pressure measurements. The chapter gives a detailed description of the sensors chosen for the task and their interfacing to the control unit. The used position estimation algorithm and its MATLAB implementation is explained in detail. After the input (raw measurement data) is analyzed, the simulated position estimation results are evaluated. Based on the results, several remarks and development possibilities are presented.
As a finishing thought the thesis gives a brief summary of the position control unit intended to be used in the future.