This thesis discusses the evaluation, implementation, and testing of several navigation algorithms and feature extraction algorithms using an inertial measurement unit (IMU) and an image capture device (camera) mounted on a ground robot and a quadrotor UAV. The vision-aided navigation algorithms are implemented on data-collected from sensors on an unmanned ground vehicle and a quadrotor, and the results are validated by comparison with GPS data. The thesis investigates sensor fusion techniques for integrating measured IMU data with information extracted from image processing algorithms in order to provide accurate vehicle state estimation. This image-based information takes the forms of features, such as corners, that are tracked over multiple image frames. An extended Kalman filter (EKF) in implemented to fuse vision and IMU data. The main goal of the work is to provide navigation of mobile robots in GPS-denied environments such as indoor environments, cluttered urban environments, or space environments such as asteroids, other planets or the moon. The experimental results show that combining pose information extracted from IMU readings along with pose information extracted from a vision-based algorithm managed to solve the drift problem that comes from using IMU alone and the scale problem that comes from using a monocular vision-based algorithm alone.
","abstract_html":"<p>This thesis discusses the evaluation, implementation, and testing of several navigation algorithms and feature extraction algorithms using an inertial measurement unit (IMU) and an image capture device (camera) mounted on a ground robot and a quadrotor UAV. The vision-aided navigation algorithms are implemented on data-collected from sensors on an unmanned ground vehicle and a quadrotor, and the results are validated by comparison with GPS data. The thesis investigates sensor fusion techniques for integrating measured IMU data with information extracted from image processing algorithms in order to provide accurate vehicle state estimation. This image-based information takes the forms of features, such as corners, that are tracked over multiple image frames. An extended Kalman filter (EKF) in implemented to fuse vision and IMU data. The main goal of the work is to provide navigation of mobile robots in GPS-denied environments such as indoor environments, cluttered urban environments, or space environments such as asteroids, other planets or the moon. The experimental results show that combining pose information extracted from IMU readings along with pose information extracted from a vision-based algorithm managed to solve the drift problem that comes from using IMU alone and the scale problem that comes from using a monocular vision-based algorithm alone.</p>","abstract_has_math":false,"creators":["Sayem, Ahmed Saber Soliman"],"institution":null,"degree_name":"Master of Science in Unmanned and Autonomous Systems Engineering","degree_level":"Thesis - Open Access","degree_discipline":"Electrical, Computer, Software, and Systems Engineering","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-08-01T07:00:00Z","date_published":"2016-08-01T07:00:00Z","updated_at":"2026-07-27T19:26:02Z","subjects":["vision-aided","navigation","autonomous vehicles","Automotive Engineering","Navigation, Guidance, Control and Dynamics"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://commons.erau.edu/edt/240","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Sayem, Ahmed Saber Soliman"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical, Computer, Software, and Systems Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis - Open Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science in Unmanned and Autonomous Systems Engineering"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["vision-aided","navigation","autonomous vehicles","Automotive Engineering","Navigation, Guidance, Control and Dynamics"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://commons.erau.edu/edt/240"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This thesis discusses the evaluation, implementation, and testing of several navigation algorithms and feature extraction algorithms using an inertial measurement unit (IMU) and an image capture device (camera) mounted on a ground robot and a quadrotor UAV. The vision-aided navigation algorithms are implemented on data-collected from sensors on an unmanned ground vehicle and a quadrotor, and the results are validated by comparison with GPS data. The thesis investigates sensor fusion techniques for integrating measured IMU data with information extracted from image processing algorithms in order to provide accurate vehicle state estimation. This image-based information takes the forms of features, such as corners, that are tracked over multiple image frames. An extended Kalman filter (EKF) in implemented to fuse vision and IMU data. The main goal of the work is to provide navigation of mobile robots in GPS-denied environments such as indoor environments, cluttered urban environments, or space environments such as asteroids, other planets or the moon. The experimental results show that combining pose information extracted from IMU readings along with pose information extracted from a vision-based algorithm managed to solve the drift problem that comes from using IMU alone and the scale problem that comes from using a monocular vision-based algorithm alone.
"]},{"key":"dc:title","label":"Title","values":["Vision-Aided Navigation for Autonomous Vehicles Using Tracked Feature Points"]}]}],"canonical_facts":{"dc:creator":["Sayem, Ahmed Saber Soliman"],"dc:description.abstract":["This thesis discusses the evaluation, implementation, and testing of several navigation algorithms and feature extraction algorithms using an inertial measurement unit (IMU) and an image capture device (camera) mounted on a ground robot and a quadrotor UAV. The vision-aided navigation algorithms are implemented on data-collected from sensors on an unmanned ground vehicle and a quadrotor, and the results are validated by comparison with GPS data. The thesis investigates sensor fusion techniques for integrating measured IMU data with information extracted from image processing algorithms in order to provide accurate vehicle state estimation. This image-based information takes the forms of features, such as corners, that are tracked over multiple image frames. An extended Kalman filter (EKF) in implemented to fuse vision and IMU data. The main goal of the work is to provide navigation of mobile robots in GPS-denied environments such as indoor environments, cluttered urban environments, or space environments such as asteroids, other planets or the moon. The experimental results show that combining pose information extracted from IMU readings along with pose information extracted from a vision-based algorithm managed to solve the drift problem that comes from using IMU alone and the scale problem that comes from using a monocular vision-based algorithm alone.
"],"dc:identifier":["https://commons.erau.edu/edt/240"],"dc:subject":["vision-aided","navigation","autonomous vehicles","Automotive Engineering","Navigation, Guidance, Control and Dynamics"],"dc:title":["Vision-Aided Navigation for Autonomous Vehicles Using Tracked Feature Points"],"thesis:degree_discipline":["Electrical, Computer, Software, and Systems Engineering"],"thesis:degree_level":["Thesis - Open Access"],"thesis:degree_name":["Master of Science in Unmanned and Autonomous Systems Engineering"]},"updated_at":"2026-07-27T19:26:02Z"}