Precise pose information is a fundamental prerequisite for numerous applications in robotics, Artificial Intelligent and mobile computing. Many well-developed algorithms have been established using a single sensor or multiple sensors. Visual Inertial Odometry (VIO) uses images and inertial measurements to estimate the motion and is considered a key technology for GPS-denied localization in the real world and also virtual reality and augmented reality.
This study develops three novel learning-based approaches to Odometry estimation using a monocular camera and inertial measurement unit. The networks are well-trained on standard datasets, KITTI and EuROC, and a custom dataset using supervised, unsupervised and semi-supervised training methods. Compared to traditional methods, the deep-learning methods presented here do not require precise manual synchronization of the camera and IMU or explicit camera calibration.
To the best of our knowledge, the proposed supervised method is a novel end-to-end trainable Visual-Inertial Odometry method with an IMU pre-integration module,that simplifies the network architecture and reduces the computation cost. Meanwhile, the unsupervised Visual-Inertial Odometry method shows its novelty in achieving outstanding accuracy in Odometry estimation while training with monocular images and inertial measurements only. Last but not least, the semi-supervised method is the first VisualInertial Odometry approach that uses a semi-supervised training technique in the literature, allowing the network to learn from both labeled and unlabeled datasets.
Through our qualitative and quantitative experimentation on a wide range of datasets, we conclude that the proposed methods can be used to obtain accurate visual localization information to a wide variety of consumer devices and robotic platforms.
","abstract_html":"<p>Precise pose information is a fundamental prerequisite for numerous applications in robotics, Artificial Intelligent and mobile computing. Many well-developed algorithms have been established using a single sensor or multiple sensors. Visual Inertial Odometry (VIO) uses images and inertial measurements to estimate the motion and is considered a key technology for GPS-denied localization in the real world and also virtual reality and augmented reality.</p> <p>This study develops three novel learning-based approaches to Odometry estimation using a monocular camera and inertial measurement unit. The networks are well-trained on standard datasets, KITTI and EuROC, and a custom dataset using supervised, unsupervised and semi-supervised training methods. Compared to traditional methods, the deep-learning methods presented here do not require precise manual synchronization of the camera and IMU or explicit camera calibration.</p> <p>To the best of our knowledge, the proposed supervised method is a novel end-to-end trainable Visual-Inertial Odometry method with an IMU pre-integration module,that simplifies the network architecture and reduces the computation cost. Meanwhile, the unsupervised Visual-Inertial Odometry method shows its novelty in achieving outstanding accuracy in Odometry estimation while training with monocular images and inertial measurements only. Last but not least, the semi-supervised method is the first VisualInertial Odometry approach that uses a semi-supervised training technique in the literature, allowing the network to learn from both labeled and unlabeled datasets.</p> <p>Through our qualitative and quantitative experimentation on a wide range of datasets, we conclude that the proposed methods can be used to obtain accurate visual localization information to a wide variety of consumer devices and robotic platforms.</p>","abstract_has_math":false,"creators":["Tian, Yuan"],"institution":null,"degree_name":"Doctor of Philosophy in Mechanical Engineering","degree_level":"Dissertation - Open Access","degree_discipline":"Mechanical Engineering","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-07-01T07:00:00Z","date_published":"2020-07-01T07:00:00Z","updated_at":"2026-07-27T19:26:34Z","subjects":["monocular","inertial","odometry","Mechanical Engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://commons.erau.edu/edt/527","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Tian, Yuan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation - Open Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy in Mechanical Engineering"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["monocular","inertial","odometry","Mechanical Engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://commons.erau.edu/edt/527"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Precise pose information is a fundamental prerequisite for numerous applications in robotics, Artificial Intelligent and mobile computing. Many well-developed algorithms have been established using a single sensor or multiple sensors. Visual Inertial Odometry (VIO) uses images and inertial measurements to estimate the motion and is considered a key technology for GPS-denied localization in the real world and also virtual reality and augmented reality.
This study develops three novel learning-based approaches to Odometry estimation using a monocular camera and inertial measurement unit. The networks are well-trained on standard datasets, KITTI and EuROC, and a custom dataset using supervised, unsupervised and semi-supervised training methods. Compared to traditional methods, the deep-learning methods presented here do not require precise manual synchronization of the camera and IMU or explicit camera calibration.
To the best of our knowledge, the proposed supervised method is a novel end-to-end trainable Visual-Inertial Odometry method with an IMU pre-integration module,that simplifies the network architecture and reduces the computation cost. Meanwhile, the unsupervised Visual-Inertial Odometry method shows its novelty in achieving outstanding accuracy in Odometry estimation while training with monocular images and inertial measurements only. Last but not least, the semi-supervised method is the first VisualInertial Odometry approach that uses a semi-supervised training technique in the literature, allowing the network to learn from both labeled and unlabeled datasets.
Through our qualitative and quantitative experimentation on a wide range of datasets, we conclude that the proposed methods can be used to obtain accurate visual localization information to a wide variety of consumer devices and robotic platforms.
"]},{"key":"dc:title","label":"Title","values":["Monocular Visual Inertial Odometry using Learning-based Methods"]}]}],"canonical_facts":{"dc:creator":["Tian, Yuan"],"dc:description.abstract":["Precise pose information is a fundamental prerequisite for numerous applications in robotics, Artificial Intelligent and mobile computing. Many well-developed algorithms have been established using a single sensor or multiple sensors. Visual Inertial Odometry (VIO) uses images and inertial measurements to estimate the motion and is considered a key technology for GPS-denied localization in the real world and also virtual reality and augmented reality.
This study develops three novel learning-based approaches to Odometry estimation using a monocular camera and inertial measurement unit. The networks are well-trained on standard datasets, KITTI and EuROC, and a custom dataset using supervised, unsupervised and semi-supervised training methods. Compared to traditional methods, the deep-learning methods presented here do not require precise manual synchronization of the camera and IMU or explicit camera calibration.
To the best of our knowledge, the proposed supervised method is a novel end-to-end trainable Visual-Inertial Odometry method with an IMU pre-integration module,that simplifies the network architecture and reduces the computation cost. Meanwhile, the unsupervised Visual-Inertial Odometry method shows its novelty in achieving outstanding accuracy in Odometry estimation while training with monocular images and inertial measurements only. Last but not least, the semi-supervised method is the first VisualInertial Odometry approach that uses a semi-supervised training technique in the literature, allowing the network to learn from both labeled and unlabeled datasets.
Through our qualitative and quantitative experimentation on a wide range of datasets, we conclude that the proposed methods can be used to obtain accurate visual localization information to a wide variety of consumer devices and robotic platforms.
"],"dc:identifier":["https://commons.erau.edu/edt/527"],"dc:subject":["monocular","inertial","odometry","Mechanical Engineering"],"dc:title":["Monocular Visual Inertial Odometry using Learning-based Methods"],"thesis:degree_discipline":["Mechanical Engineering"],"thesis:degree_level":["Dissertation - Open Access"],"thesis:degree_name":["Doctor of Philosophy in Mechanical Engineering"]},"updated_at":"2026-07-27T19:26:34Z"}