{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/121977"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/121977","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Polarization-based underwater geolocalization with neural network models","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-03-01 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-03-01 without embargo terms","abstract_has_math":false,"creators":["Bai, Xiaoyang"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Schwing, Alexander","Gruev, Viktor","Forsyth, David","Laze, Svetlana","Schechner, Yoav"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-12","date_published":"2023-12","updated_at":"2026-07-22T22:25:00Z","subjects":["Underwater Geolocalization","Polarization-based Underwater Geolocalization","Machine Learning","Computer Vision","Neural Network"],"languages":["en","eng"],"rights":["Copyright 2023 Xiaoyang Bai"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/121977","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Schwing, Alexander","Gruev, Viktor","Forsyth, David","Laze, Svetlana","Schechner, Yoav"]},{"key":"dc:creator","label":"Author","values":["Bai, Xiaoyang"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2023-12","2023-11-13"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Underwater Geolocalization","Polarization-based Underwater Geolocalization","Machine Learning","Computer Vision","Neural Network"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2023 Xiaoyang Bai"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/121977"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-03-01 without embargo terms","The student, Xiaoyang Bai, accepted the attached license on 2023-11-10 at 10:18.","The student, Xiaoyang Bai, submitted this Dissertation for approval on 2023-11-10 at 10:28.","This Dissertation was approved for publication on 2023-11-13 at 14:04.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19902 on 2024-03-01 at 13:14:15","Understanding the sea has always been a core theme of mankind's scientific endeavors. In recent years, the spatial and temporal scope of such exploration has been further expanded thanks to the rapid development of robot technologies. However, due to the unavailability of GPS signals underwater, efficient, high-accuracy and real-time navigation of man-made autonomous devices below the water surface remains an open challenge. Inspired by the visual system of marine animals such as mantis shrimps, polarization-based underwater geolocalization (PUG) aims to solve this problem by leveraging the rich polarization patterns generated by sunlight and moonlight as they are refracted and scattered underwater. Those patterns encode information about the celestial body's location in the sky, and once such information is extracted, one can subsequently combine it with the coordinated date and time when the observation is made to infer the observer's geolocation. In this thesis, we present a deep learning-based approach to PUG, which features higher accuracy, lower latency and better applicability to real-world scenarios than the existing parametric method. As a preliminary step, we develop a calibration algorithm to resolve the inherent angle of polarization (AoP) shift when the polarization camera is equipped with a wide-angle lens. This calibration algorithm enables us to use omnidirectional polarization images for training and evaluating our PUG methods. Next, we establish that rotation invariance and temporal modeling are the keys to designing network models that estimate sun location from omnidirectional polarization images. To this end, we propose RI-ResNet and RDM to address the two key points respectively and construct our learning-based PUG method by joining them with a particle filter (PF) geolocation estimator. We demonstrate that our method can learn to geolocalize in different sites, seasons, water types and depths. We have also proved the feasibility of PUG at nighttime for the first time ever. Finally, we focus our attention on improving model generalizability by proposing SecTran-M, a rotation invariant Transformer network, as the sun localization backbone and using unscented Kalman filter (UKF) for temporal modeling. Evaluation results on cross-site tasks show that our locally-trained model can achieve coarse-level geolocalization on a global scale, taking us one step closer to a globally functional high-accuracy PUG method that we have always envisioned."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Polarization-based underwater geolocalization with neural network models"]}]}],"canonical_facts":{"dc:contributor":["Schwing, Alexander","Gruev, Viktor","Forsyth, David","Laze, Svetlana","Schechner, Yoav"],"dc:creator":["Bai, Xiaoyang"],"dc:date":["2023-12","2023-11-13"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-03-01 without embargo terms","The student, Xiaoyang Bai, accepted the attached license on 2023-11-10 at 10:18.","The student, Xiaoyang Bai, submitted this Dissertation for approval on 2023-11-10 at 10:28.","This Dissertation was approved for publication on 2023-11-13 at 14:04.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19902 on 2024-03-01 at 13:14:15","Understanding the sea has always been a core theme of mankind's scientific endeavors. In recent years, the spatial and temporal scope of such exploration has been further expanded thanks to the rapid development of robot technologies. However, due to the unavailability of GPS signals underwater, efficient, high-accuracy and real-time navigation of man-made autonomous devices below the water surface remains an open challenge. Inspired by the visual system of marine animals such as mantis shrimps, polarization-based underwater geolocalization (PUG) aims to solve this problem by leveraging the rich polarization patterns generated by sunlight and moonlight as they are refracted and scattered underwater. Those patterns encode information about the celestial body's location in the sky, and once such information is extracted, one can subsequently combine it with the coordinated date and time when the observation is made to infer the observer's geolocation. In this thesis, we present a deep learning-based approach to PUG, which features higher accuracy, lower latency and better applicability to real-world scenarios than the existing parametric method. As a preliminary step, we develop a calibration algorithm to resolve the inherent angle of polarization (AoP) shift when the polarization camera is equipped with a wide-angle lens. This calibration algorithm enables us to use omnidirectional polarization images for training and evaluating our PUG methods. Next, we establish that rotation invariance and temporal modeling are the keys to designing network models that estimate sun location from omnidirectional polarization images. To this end, we propose RI-ResNet and RDM to address the two key points respectively and construct our learning-based PUG method by joining them with a particle filter (PF) geolocation estimator. We demonstrate that our method can learn to geolocalize in different sites, seasons, water types and depths. We have also proved the feasibility of PUG at nighttime for the first time ever. Finally, we focus our attention on improving model generalizability by proposing SecTran-M, a rotation invariant Transformer network, as the sun localization backbone and using unscented Kalman filter (UKF) for temporal modeling. Evaluation results on cross-site tasks show that our locally-trained model can achieve coarse-level geolocalization on a global scale, taking us one step closer to a globally functional high-accuracy PUG method that we have always envisioned."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/121977"],"dc:language":["en","eng"],"dc:rights":["Copyright 2023 Xiaoyang Bai"],"dc:subject":["Underwater Geolocalization","Polarization-based Underwater Geolocalization","Machine Learning","Computer Vision","Neural Network"],"dc:title":["Polarization-based underwater geolocalization with neural network models"],"dc:type":["text"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:00Z"}