Estimation of Opti-track motion capture system data

Abstract

The background of this FYP project is that the Opti-track motion capture system can provide measured coordinated data signals of the tracked object. The aim is designing the mathematical model of Kalman Filter to estimate the optimized positional data for localization and estimate the velocity and acceleration. The problems are the measured 6DoF data of the drone are affected by the noise. Thus, it is necessary to use Kalman Filter as a noise reduction algorithm to estimate the optimized positional data of the drone. It is necessary to look into these problems because it can help users better understanding the flying condition of the drone and avoiding over speeding of drone or any collision happening. The study was conducted by learning how to operate the Opti-track system by reading the user manuals. Then, I designed the methodologies for writing a MATLAB program to perform localization and estimation with Kalman Filter. The result is that, based on the optimized estimated positional data of the drone using Kalman Filter. The program successfully generates 3D animated graph for localization which shows the location and moving path of the drone in real time. Also, the program successfully applies Kalman Filter in the process of estimation of velocity and acceleration of the drone. The estimation results are less affected by noise and more accurate. In conclusion, the software successfully performs localization and estimation of data.