The applications of visual sensing techniques have revolutionized the way autonomous systems perceive their environment on Earth. In space, the challenge of accurate perception has proven to be a difficult task. Due to adverse lighting conditions, high-noise images are common and degrade the performance of traditional feature-based estimation and perception algorithms. This work explores the applications of a variational filtering scheme founded in Lie Group theory to an autonomous rendezvous, proximity operations and docking problem. Two methodologies, a Monte Carlo approach and an Unscented Transform, for propagating uncertainty using a Lie Group Variational Filter are introduced and developed.
","abstract_html":"<p>The applications of visual sensing techniques have revolutionized the way autonomous systems perceive their environment on Earth. In space, the challenge of accurate perception has proven to be a difficult task. Due to adverse lighting conditions, high-noise images are common and degrade the performance of traditional feature-based estimation and perception algorithms. This work explores the applications of a variational filtering scheme founded in Lie Group theory to an autonomous rendezvous, proximity operations and docking problem. Two methodologies, a Monte Carlo approach and an Unscented Transform, for propagating uncertainty using a Lie Group Variational Filter are introduced and developed.</p>","abstract_has_math":false,"creators":["Hays, Christopher W."],"institution":null,"degree_name":"Master of Science in Aerospace Engineering","degree_level":"Thesis - Open Access","degree_discipline":"Aerospace Engineering","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-10-01T07:00:00Z","date_published":"2021-10-01T07:00:00Z","updated_at":"2026-07-27T19:25:16Z","subjects":["visual sensing","variational filtering","Lie Group theory","Aerospace Engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://commons.erau.edu/edt/565","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Hays, Christopher W."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Aerospace Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis - Open Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science in Aerospace Engineering"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["visual sensing","variational filtering","Lie Group theory","Aerospace Engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://commons.erau.edu/edt/565"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The applications of visual sensing techniques have revolutionized the way autonomous systems perceive their environment on Earth. In space, the challenge of accurate perception has proven to be a difficult task. Due to adverse lighting conditions, high-noise images are common and degrade the performance of traditional feature-based estimation and perception algorithms. This work explores the applications of a variational filtering scheme founded in Lie Group theory to an autonomous rendezvous, proximity operations and docking problem. Two methodologies, a Monte Carlo approach and an Unscented Transform, for propagating uncertainty using a Lie Group Variational Filter are introduced and developed.
"]},{"key":"dc:title","label":"Title","values":["Relative Pose Uncertainty Quantification Using Lie Group Variational Filtering"]}]}],"canonical_facts":{"dc:creator":["Hays, Christopher W."],"dc:description.abstract":["The applications of visual sensing techniques have revolutionized the way autonomous systems perceive their environment on Earth. In space, the challenge of accurate perception has proven to be a difficult task. Due to adverse lighting conditions, high-noise images are common and degrade the performance of traditional feature-based estimation and perception algorithms. This work explores the applications of a variational filtering scheme founded in Lie Group theory to an autonomous rendezvous, proximity operations and docking problem. Two methodologies, a Monte Carlo approach and an Unscented Transform, for propagating uncertainty using a Lie Group Variational Filter are introduced and developed.
"],"dc:identifier":["https://commons.erau.edu/edt/565"],"dc:subject":["visual sensing","variational filtering","Lie Group theory","Aerospace Engineering"],"dc:title":["Relative Pose Uncertainty Quantification Using Lie Group Variational Filtering"],"thesis:degree_discipline":["Aerospace Engineering"],"thesis:degree_level":["Thesis - Open Access"],"thesis:degree_name":["Master of Science in Aerospace Engineering"]},"updated_at":"2026-07-27T19:25:16Z"}