Underwater localization is a difficult task when it comes to making the system autonomous due to the unpredictable environment. The fact that radio signals such as GPS cannot be transmitted through water makes autonomous movement and localization underwater even more challenging. One specific method that is widely used for autonomous underwater navigation applications is Simultaneous Localization and Mapping (SLAM), a technique in which a map is created and updated while localizing the vehicle within the map. In SLAM, feature detection is used in landmark extraction and data association by examining each pixel and differentiating landmarks pixels from those of the background. Previous research on the performance of different feature detection methods have been done in environments such as cisterns and caverns where the effects of the ocean are reduced. Our objective, however, is to achieves robust localization in the open ocean environment of the Cal Poly pier. This thesis performs a comparative study between different feature detection methods including Scale Invariant Feature Transform (SIFT), Speeded-Up Robust Features (SURF), and Oriented FAST and Rotated BRIEF (ORB) on different sensors. We evaluate the feature detection and matching performance of these algorithms in a simulated open-ocean environment.
","abstract_html":"<p>Underwater localization is a difficult task when it comes to making the system autonomous due to the unpredictable environment. The fact that radio signals such as GPS cannot be transmitted through water makes autonomous movement and localization underwater even more challenging. One specific method that is widely used for autonomous underwater navigation applications is Simultaneous Localization and Mapping (SLAM), a technique in which a map is created and updated while localizing the vehicle within the map. In SLAM, feature detection is used in landmark extraction and data association by examining each pixel and differentiating landmarks pixels from those of the background. Previous research on the performance of different feature detection methods have been done in environments such as cisterns and caverns where the effects of the ocean are reduced. Our objective, however, is to achieves robust localization in the open ocean environment of the Cal Poly pier. This thesis performs a comparative study between different feature detection methods including Scale Invariant Feature Transform (SIFT), Speeded-Up Robust Features (SURF), and Oriented FAST and Rotated BRIEF (ORB) on different sensors. We evaluate the feature detection and matching performance of these algorithms in a simulated open-ocean environment.</p>","abstract_has_math":false,"creators":["Kim, Andrew Y"],"institution":null,"degree_name":"MS in Computer Science","degree_level":null,"degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Lynne Slivovsky"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-06-01T07:00:00Z","date_published":"2018-06-01T07:00:00Z","updated_at":"2026-07-24T01:31:56Z","subjects":["Underwater Navigation","Autonomous","SLAM","AUV","Feature Detection","Localization","Robotics"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["10.15368/theses.2018.49"],"render_values":[{"text":"10.15368/theses.2018.49","href":"https://doi.org/10.15368/theses.2018.49","code":true}]}]},"links":{"outbound_url":"https://digitalcommons.calpoly.edu/theses/1852","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Lynne Slivovsky"]},{"key":"dc:creator","label":"Author","values":["Kim, Andrew Y"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2018-06-13T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_name","label":"Degree Name","values":["MS in Computer Science"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Underwater Navigation","Autonomous","SLAM","AUV","Feature Detection","Localization","Robotics"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.calpoly.edu/theses/1852","10.15368/theses.2018.49"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Underwater localization is a difficult task when it comes to making the system autonomous due to the unpredictable environment. The fact that radio signals such as GPS cannot be transmitted through water makes autonomous movement and localization underwater even more challenging. One specific method that is widely used for autonomous underwater navigation applications is Simultaneous Localization and Mapping (SLAM), a technique in which a map is created and updated while localizing the vehicle within the map. In SLAM, feature detection is used in landmark extraction and data association by examining each pixel and differentiating landmarks pixels from those of the background. Previous research on the performance of different feature detection methods have been done in environments such as cisterns and caverns where the effects of the ocean are reduced. Our objective, however, is to achieves robust localization in the open ocean environment of the Cal Poly pier. This thesis performs a comparative study between different feature detection methods including Scale Invariant Feature Transform (SIFT), Speeded-Up Robust Features (SURF), and Oriented FAST and Rotated BRIEF (ORB) on different sensors. We evaluate the feature detection and matching performance of these algorithms in a simulated open-ocean environment.
"]},{"key":"dc:title","label":"Title","values":["A Comparative Study of Feature Detection Methods for AUV Localization"]}]}],"canonical_facts":{"dc:contributor":["Lynne Slivovsky"],"dc:creator":["Kim, Andrew Y"],"dc:date.available":["2018-06-13T07:00:00Z"],"dc:description.abstract":["Underwater localization is a difficult task when it comes to making the system autonomous due to the unpredictable environment. The fact that radio signals such as GPS cannot be transmitted through water makes autonomous movement and localization underwater even more challenging. One specific method that is widely used for autonomous underwater navigation applications is Simultaneous Localization and Mapping (SLAM), a technique in which a map is created and updated while localizing the vehicle within the map. In SLAM, feature detection is used in landmark extraction and data association by examining each pixel and differentiating landmarks pixels from those of the background. Previous research on the performance of different feature detection methods have been done in environments such as cisterns and caverns where the effects of the ocean are reduced. Our objective, however, is to achieves robust localization in the open ocean environment of the Cal Poly pier. This thesis performs a comparative study between different feature detection methods including Scale Invariant Feature Transform (SIFT), Speeded-Up Robust Features (SURF), and Oriented FAST and Rotated BRIEF (ORB) on different sensors. We evaluate the feature detection and matching performance of these algorithms in a simulated open-ocean environment.
"],"dc:identifier":["https://digitalcommons.calpoly.edu/theses/1852","10.15368/theses.2018.49"],"dc:subject":["Underwater Navigation","Autonomous","SLAM","AUV","Feature Detection","Localization","Robotics"],"dc:title":["A Comparative Study of Feature Detection Methods for AUV Localization"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_name":["MS in Computer Science"]},"updated_at":"2026-07-24T01:31:56Z"}