{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/99412"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/99412","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Hardware and software considerations for monocular SLAM in a riverine environment","abstract":"A monocular SLAM algorithm for use on rivers is proposed and compared to existing methods using a newly created SLAM dataset. The proposed algorithm uses a single camera and inertial measurements to estimate the location of a canoe and a map of a river simultaneously using an extended Kalman filter. The algorithm exploits the reflections of map landmarks in the river in order to obtain a depth estimate from a single view. Landmark reflections are found by using the state covariance matrix of the extended Kalman filter to define a search region where reflections are likely to be found. A process noise model is proposed to more accurately reflect the noise characteristics of the inertial measurement unit. The dataset used for the experiments was collected from a canoe on the Sangamon River covering 2.7 kilometers in 44 minutes and divided into eight subsets. Data collected includes stereo images, inertial measurements, and GPS position data for ground truth. The proposed algorithm is evaluated by measuring the translation and attitude error with respect to ground truth and comparisons are made to the stereo method, ORB-SLAM2.","abstract_html":"A monocular SLAM algorithm for use on rivers is proposed and compared to existing methods using a newly created SLAM dataset. The proposed algorithm uses a single camera and inertial measurements to estimate the location of a canoe and a map of a river simultaneously using an extended Kalman filter. The algorithm exploits the reflections of map landmarks in the river in order to obtain a depth estimate from a single view. Landmark reflections are found by using the state covariance matrix of the extended Kalman filter to define a search region where reflections are likely to be found. A process noise model is proposed to more accurately reflect the noise characteristics of the inertial measurement unit. The dataset used for the experiments was collected from a canoe on the Sangamon River covering 2.7 kilometers in 44 minutes and divided into eight subsets. Data collected includes stereo images, inertial measurements, and GPS position data for ground truth. The proposed algorithm is evaluated by measuring the translation and attitude error with respect to ground truth and comparisons are made to the stereo method, ORB-SLAM2.","abstract_has_math":false,"creators":["Miller, Martin Hudson"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Hutchinson, Seth A.","Chung, Soon-Jo"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-03-13T15:49:12Z","date_published":"2018-03-13T15:49:12Z","updated_at":"2026-07-22T22:24:37Z","subjects":["Simultaneous localization and mapping (SLAM)","Monocular","Stereo","River","Inertial measurement unit (IMU)","Camera","Robotics","Inertial"],"languages":["en"],"rights":["Copyright 2017 Martin Miller"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/99412","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Hutchinson, Seth A.","Chung, Soon-Jo"]},{"key":"dc:creator","label":"Author","values":["Miller, Martin Hudson"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-03-13T15:49:12Z","2017-12-12","2017-12"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"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":["Simultaneous localization and mapping (SLAM)","Monocular","Stereo","River","Inertial measurement unit (IMU)","Camera","Robotics","Inertial"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2017 Martin Miller"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/99412"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["A monocular SLAM algorithm for use on rivers is proposed and compared to existing methods using a newly created SLAM dataset. The proposed algorithm uses a single camera and inertial measurements to estimate the location of a canoe and a map of a river simultaneously using an extended Kalman filter. The algorithm exploits the reflections of map landmarks in the river in order to obtain a depth estimate from a single view. Landmark reflections are found by using the state covariance matrix of the extended Kalman filter to define a search region where reflections are likely to be found. A process noise model is proposed to more accurately reflect the noise characteristics of the inertial measurement unit. The dataset used for the experiments was collected from a canoe on the Sangamon River covering 2.7 kilometers in 44 minutes and divided into eight subsets. Data collected includes stereo images, inertial measurements, and GPS position data for ground truth. The proposed algorithm is evaluated by measuring the translation and attitude error with respect to ground truth and comparisons are made to the stereo method, ORB-SLAM2.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2018-03-13 without embargo terms","The student, Martin Miller, accepted the attached license on 2017-12-11 at 08:44.","The student, Martin Miller, submitted this Thesis for approval on 2017-12-11 at 17:59.","This Thesis was approved for publication on 2017-12-12 at 08:29.","DSpace SAF Submission Ingestion Package generated from Vireo submission #11936 on 2018-03-13 at 10:12:06","Made available in DSpace on 2018-03-13T15:49:12Z (GMT). No. of bitstreams: 4 MILLER-THESIS-2017.pdf: 5303426 bytes, checksum: 12425a098c50973b3b866010db6293f9 (MD5) thesis.zip: 5516589 bytes, checksum: 04f9f6b8e76b117760f4f673d48cb17f (MD5) LICENSE.txt: 4210 bytes, checksum: bcd31b59fd9c57a06827db5db13b8ab4 (MD5) rights.html: 63169 bytes, checksum: 2eae1c104b5c7747a48f594f85b7975b (MD5) Previous issue date: 2017-12-12"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Hardware and software considerations for monocular SLAM in a riverine environment"]}]}],"canonical_facts":{"dc:contributor":["Hutchinson, Seth A.","Chung, Soon-Jo"],"dc:creator":["Miller, Martin Hudson"],"dc:date":["2018-03-13T15:49:12Z","2017-12-12","2017-12"],"dc:description":["A monocular SLAM algorithm for use on rivers is proposed and compared to existing methods using a newly created SLAM dataset. The proposed algorithm uses a single camera and inertial measurements to estimate the location of a canoe and a map of a river simultaneously using an extended Kalman filter. The algorithm exploits the reflections of map landmarks in the river in order to obtain a depth estimate from a single view. Landmark reflections are found by using the state covariance matrix of the extended Kalman filter to define a search region where reflections are likely to be found. A process noise model is proposed to more accurately reflect the noise characteristics of the inertial measurement unit. The dataset used for the experiments was collected from a canoe on the Sangamon River covering 2.7 kilometers in 44 minutes and divided into eight subsets. Data collected includes stereo images, inertial measurements, and GPS position data for ground truth. The proposed algorithm is evaluated by measuring the translation and attitude error with respect to ground truth and comparisons are made to the stereo method, ORB-SLAM2.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2018-03-13 without embargo terms","The student, Martin Miller, accepted the attached license on 2017-12-11 at 08:44.","The student, Martin Miller, submitted this Thesis for approval on 2017-12-11 at 17:59.","This Thesis was approved for publication on 2017-12-12 at 08:29.","DSpace SAF Submission Ingestion Package generated from Vireo submission #11936 on 2018-03-13 at 10:12:06","Made available in DSpace on 2018-03-13T15:49:12Z (GMT). No. of bitstreams: 4 MILLER-THESIS-2017.pdf: 5303426 bytes, checksum: 12425a098c50973b3b866010db6293f9 (MD5) thesis.zip: 5516589 bytes, checksum: 04f9f6b8e76b117760f4f673d48cb17f (MD5) LICENSE.txt: 4210 bytes, checksum: bcd31b59fd9c57a06827db5db13b8ab4 (MD5) rights.html: 63169 bytes, checksum: 2eae1c104b5c7747a48f594f85b7975b (MD5) Previous issue date: 2017-12-12"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/99412"],"dc:language":["en"],"dc:rights":["Copyright 2017 Martin Miller"],"dc:subject":["Simultaneous localization and mapping (SLAM)","Monocular","Stereo","River","Inertial measurement unit (IMU)","Camera","Robotics","Inertial"],"dc:title":["Hardware and software considerations for monocular SLAM in a riverine environment"],"dc:type":["text"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:37Z"}