Multi-Sensor Fusion for Navigation of Ground Vehicles

Abstract

Navigation is a core requirement for autonomous vehicles and robotics. The objective of this thesis is to compute the navigation solution of a ground vehicle by fusing data from Inertial Navigation System (INS), Visual Odometry (VO), and Global Positioning System (GPS) using a Dual Extended Kalman Filter (DEKF) algorithm. The research in this thesis is conducted in three phases. The first phase deals with the development of a VO navigation system. In this phase the traditional Stereo Visual Odometry (SVO) methodology is analyzed, and an improvement is proposed at the pose estimation and pose optimization stages to present the Modified Stereo Visual Odometry (ModSVO) algorithm. The second phase deals with the development of INS/VO and INS/GPS integrated systems using EKF. It is shown that while accuracy improves compared to standalone sensors, but in case of VO or GPS failure the accuracy deteriorates. The third phase presents a solution to this problem by developing the INS/VO/GPS system using a Dual Extended Kalman Filter (DEKF) scheme. It is shown that the INS/VO/GPS system outperforms INS/VO and INS/GPS systems in cases of VO failure or GPS failure.iii I am deeply grateful to my Parents, who encouraged and supported me to take this step for obtaining master's degree and stood by me throughout this journey.I would like to thank my Parents-in-Law, who spent their time and effort praying for me and inspiring me with words of encouragement.To my wife Hira, words cannot express how invaluable your presence has been throughout this time. I could not have completed this journey without your love and support, and for that, I would like to wholeheartedly say, thank you.