{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/132545"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/132545","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Toward reliable localization: exploring infrared fiducial markers and multi-imu fusion as alternative sensor modalities","abstract":"Reliable localization remains a fundamental challenge for autonomous systems operating in environments where traditional sensors underperform. This dissertation explores two alternative sensor modalities: infrared fiducial markers and multi-IMU integration, as means to enhance localization robustness in GNSS- and vision-degraded settings. Vision-based navigation systems, which commonly rely on visible-spectrum cameras and fiducial markers (e.g., AprilTags or ArUco markers), are widely used for estimating position and orientation. However, their performance degrades significantly under poor lighting conditions, occlusions, motion blur, and adverse weather. These limitations make them unreliable in critical scenarios such as nighttime operations or foggy environments. To overcome these challenges, this work proposes an alternative pose estimation framework based on thermal infrared imagery. In addition, low-cost MEMS IMUs typically used in these systems suffer from high noise and bias instability, limiting the overall accuracy and robustness of the pose estimation. To overcome these challenges, this work proposes two alternative approaches: one based on thermal infrared marker, and another based on fusing measurements from multiple IMUs to improve inertial sensing. In the marker-based approach, a passive infrared fiducial marker is introduced, designed using materials with distinct thermal emissivity properties to remain visible under challenging conditions such as low light, fog, and nighttime. A learning-based detection method is developed by adapting an existing visible-spectrum marker detection algorithm to the infrared domain, addressing challenges such as thermal reflections and partial occlusions. To further enhance detection accuracy, a Pix2Pix image-to-image translation model is used as a pre-processing step to remove thermal reflections from infrared imagery. This pre-processing improves input quality and supports a variety of marker designs. Separately, this work investigates the fusion of measurements from multiple low-cost MEMS IMUs to achieve noise characteristics comparable to those of tactical-grade inertial sensors. The fusion process synchronizes angular velocity and acceleration measurements, and its performance is evaluated through Allan variance analysis and real-world visual-inertial SLAM experiments using a wheeled ground robot. Together, these two approaches offer robust alternatives to conventional sensor modalities, enabling reliable localization in environments where GNSS, visual odometry, or single-IMU setups often fail. Applications include Urban Air Mobility, construction site monitoring, underground operations, and autonomous systems operating in GNSS-denied or visually degraded conditions.","abstract_html":"Reliable localization remains a fundamental challenge for autonomous systems operating in environments where traditional sensors underperform. This dissertation explores two alternative sensor modalities: infrared fiducial markers and multi-IMU integration, as means to enhance localization robustness in GNSS- and vision-degraded settings. Vision-based navigation systems, which commonly rely on visible-spectrum cameras and fiducial markers (e.g., AprilTags or ArUco markers), are widely used for estimating position and orientation. However, their performance degrades significantly under poor lighting conditions, occlusions, motion blur, and adverse weather. These limitations make them unreliable in critical scenarios such as nighttime operations or foggy environments. To overcome these challenges, this work proposes an alternative pose estimation framework based on thermal infrared imagery. In addition, low-cost MEMS IMUs typically used in these systems suffer from high noise and bias instability, limiting the overall accuracy and robustness of the pose estimation. To overcome these challenges, this work proposes two alternative approaches: one based on thermal infrared marker, and another based on fusing measurements from multiple IMUs to improve inertial sensing. In the marker-based approach, a passive infrared fiducial marker is introduced, designed using materials with distinct thermal emissivity properties to remain visible under challenging conditions such as low light, fog, and nighttime. A learning-based detection method is developed by adapting an existing visible-spectrum marker detection algorithm to the infrared domain, addressing challenges such as thermal reflections and partial occlusions. To further enhance detection accuracy, a Pix2Pix image-to-image translation model is used as a pre-processing step to remove thermal reflections from infrared imagery. This pre-processing improves input quality and supports a variety of marker designs. Separately, this work investigates the fusion of measurements from multiple low-cost MEMS IMUs to achieve noise characteristics comparable to those of tactical-grade inertial sensors. The fusion process synchronizes angular velocity and acceleration measurements, and its performance is evaluated through Allan variance analysis and real-world visual-inertial SLAM experiments using a wheeled ground robot. Together, these two approaches offer robust alternatives to conventional sensor modalities, enabling reliable localization in environments where GNSS, visual odometry, or single-IMU setups often fail. Applications include Urban Air Mobility, construction site monitoring, underground operations, and autonomous systems operating in GNSS-denied or visually degraded conditions.","abstract_has_math":false,"creators":["Choi, Su-Yeon"],"institution":"University of Illinois Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Aerospace Engineering","degree_department":null,"school":null,"contributors":["Bretl, Timothy","Golparvar-Fard, Mani","Tran, Huy","Stefanescu, Ramona"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-12","date_published":"2025-12","updated_at":"2026-07-22T22:25:07Z","subjects":["Fiducial Marker, Infrared camera, Localization, Pose Estimation, Multi-IMU, Sensor fusion"],"languages":["en"],"rights":["Copyright 2025 Su-Yeon Choi"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/132545","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Bretl, Timothy","Golparvar-Fard, Mani","Tran, Huy","Stefanescu, Ramona"]},{"key":"dc:creator","label":"Author","values":["Choi, Su-Yeon"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-12","2025-12-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Aerospace Engineering"]},{"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 Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Fiducial Marker, Infrared camera, Localization, Pose Estimation, Multi-IMU, Sensor fusion"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2025 Su-Yeon Choi"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/132545"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Reliable localization remains a fundamental challenge for autonomous systems operating in environments where traditional sensors underperform. This dissertation explores two alternative sensor modalities: infrared fiducial markers and multi-IMU integration, as means to enhance localization robustness in GNSS- and vision-degraded settings. Vision-based navigation systems, which commonly rely on visible-spectrum cameras and fiducial markers (e.g., AprilTags or ArUco markers), are widely used for estimating position and orientation. However, their performance degrades significantly under poor lighting conditions, occlusions, motion blur, and adverse weather. These limitations make them unreliable in critical scenarios such as nighttime operations or foggy environments. To overcome these challenges, this work proposes an alternative pose estimation framework based on thermal infrared imagery. In addition, low-cost MEMS IMUs typically used in these systems suffer from high noise and bias instability, limiting the overall accuracy and robustness of the pose estimation. To overcome these challenges, this work proposes two alternative approaches: one based on thermal infrared marker, and another based on fusing measurements from multiple IMUs to improve inertial sensing. In the marker-based approach, a passive infrared fiducial marker is introduced, designed using materials with distinct thermal emissivity properties to remain visible under challenging conditions such as low light, fog, and nighttime. A learning-based detection method is developed by adapting an existing visible-spectrum marker detection algorithm to the infrared domain, addressing challenges such as thermal reflections and partial occlusions. To further enhance detection accuracy, a Pix2Pix image-to-image translation model is used as a pre-processing step to remove thermal reflections from infrared imagery. This pre-processing improves input quality and supports a variety of marker designs. Separately, this work investigates the fusion of measurements from multiple low-cost MEMS IMUs to achieve noise characteristics comparable to those of tactical-grade inertial sensors. The fusion process synchronizes angular velocity and acceleration measurements, and its performance is evaluated through Allan variance analysis and real-world visual-inertial SLAM experiments using a wheeled ground robot. Together, these two approaches offer robust alternatives to conventional sensor modalities, enabling reliable localization in environments where GNSS, visual odometry, or single-IMU setups often fail. Applications include Urban Air Mobility, construction site monitoring, underground operations, and autonomous systems operating in GNSS-denied or visually degraded conditions.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2026-02-19 without embargo terms","The student, Su-Yeon Choi, accepted the attached license on 2025-11-30 at 09:54.","The student, Su-Yeon Choi, submitted this Dissertation for approval on 2025-12-03 at 06:50.","This Dissertation was approved for publication on 2025-12-05 at 10:42.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22983 on 2026-02-19 at 18:25:42"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Toward reliable localization: exploring infrared fiducial markers and multi-imu fusion as alternative sensor modalities"]}]}],"canonical_facts":{"dc:contributor":["Bretl, Timothy","Golparvar-Fard, Mani","Tran, Huy","Stefanescu, Ramona"],"dc:creator":["Choi, Su-Yeon"],"dc:date":["2025-12","2025-12-05"],"dc:description":["Reliable localization remains a fundamental challenge for autonomous systems operating in environments where traditional sensors underperform. This dissertation explores two alternative sensor modalities: infrared fiducial markers and multi-IMU integration, as means to enhance localization robustness in GNSS- and vision-degraded settings. Vision-based navigation systems, which commonly rely on visible-spectrum cameras and fiducial markers (e.g., AprilTags or ArUco markers), are widely used for estimating position and orientation. However, their performance degrades significantly under poor lighting conditions, occlusions, motion blur, and adverse weather. These limitations make them unreliable in critical scenarios such as nighttime operations or foggy environments. To overcome these challenges, this work proposes an alternative pose estimation framework based on thermal infrared imagery. In addition, low-cost MEMS IMUs typically used in these systems suffer from high noise and bias instability, limiting the overall accuracy and robustness of the pose estimation. To overcome these challenges, this work proposes two alternative approaches: one based on thermal infrared marker, and another based on fusing measurements from multiple IMUs to improve inertial sensing. In the marker-based approach, a passive infrared fiducial marker is introduced, designed using materials with distinct thermal emissivity properties to remain visible under challenging conditions such as low light, fog, and nighttime. A learning-based detection method is developed by adapting an existing visible-spectrum marker detection algorithm to the infrared domain, addressing challenges such as thermal reflections and partial occlusions. To further enhance detection accuracy, a Pix2Pix image-to-image translation model is used as a pre-processing step to remove thermal reflections from infrared imagery. This pre-processing improves input quality and supports a variety of marker designs. Separately, this work investigates the fusion of measurements from multiple low-cost MEMS IMUs to achieve noise characteristics comparable to those of tactical-grade inertial sensors. The fusion process synchronizes angular velocity and acceleration measurements, and its performance is evaluated through Allan variance analysis and real-world visual-inertial SLAM experiments using a wheeled ground robot. Together, these two approaches offer robust alternatives to conventional sensor modalities, enabling reliable localization in environments where GNSS, visual odometry, or single-IMU setups often fail. Applications include Urban Air Mobility, construction site monitoring, underground operations, and autonomous systems operating in GNSS-denied or visually degraded conditions.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2026-02-19 without embargo terms","The student, Su-Yeon Choi, accepted the attached license on 2025-11-30 at 09:54.","The student, Su-Yeon Choi, submitted this Dissertation for approval on 2025-12-03 at 06:50.","This Dissertation was approved for publication on 2025-12-05 at 10:42.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22983 on 2026-02-19 at 18:25:42"],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/132545"],"dc:language":["en"],"dc:rights":["Copyright 2025 Su-Yeon Choi"],"dc:subject":["Fiducial Marker, Infrared camera, Localization, Pose Estimation, Multi-IMU, Sensor fusion"],"dc:title":["Toward reliable localization: exploring infrared fiducial markers and multi-imu fusion as alternative sensor modalities"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Aerospace Engineering"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:07Z"}