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oai:www.ideals.illinois.edu:2142/100901 2023-07-11 col_2142_5131 col_2142_8888 com_2142_5130 com_2142_8887 com_2142_234

Hutchinson, Seth A. Chung, Soon-Jo Hutchinson, Seth A. Schwing, Alexander G. Do, Minh N. Meier, Kevin C. 2018-09-04T20:26:36Z 2018-09-04T20:26:36Z 2018-02-28 2018-05 In this dissertation, we present a simultaneous localization and mapping (SLAM) algorithm that uses B\'{e}zier curves as static landmark primitives rather than feature points. Our approach allows us to estimate the full 6-DOF pose of a robot while providing a sparse structured map which can be used to assist a robot in motion planning and control. We demonstrate how to reconstruct the 3-D location of curve landmarks from a stereo pair and how to compare the 3-D shape of curve landmarks between chronologically sequential stereo frames to solve the data association problem. We also present a method to combine curve landmarks for mapping purposes, resulting in a map with a continuous set of curves that contain fewer landmark states than conventional point-based SLAM algorithms. We demonstrate our algorithm's effectiveness with numerous experiments, including comparisons to existing state-of-the-art SLAM algorithms. A notable contribution of this dissertation is to apply our SLAM algorithm to a river setting to localize a canoe and create a sparse structured map of the border of a river. To accomplish this task, the dissertation presents a novel vision-based algorithm that identifies the boundary separating water from land in a river environment containing specular reflections. Our approach relies on the law of reflection. Assuming the surface of water behaves like a horizontal mirror, the border separating land from water corresponds to the border separating 3-D data which are either above or below the surface of water. We detect a river by identifying this border in a stereo camera. We start by demonstrating how to robustly estimate the normal and height of the water's surface with respect to a stereo camera. Then, we segment water from land by identifying the boundary separating dense 3-D stereo data which are either above or below the water's surface. With the border of water identified, we validate the proposed river boundary detection algorithm by applying it to a chronologically sequential video sequence obtained from the visual-inertial canoe dataset. Additionally, we use our SLAM algorithm to create a sparse structured map of the shoreline of a river. Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2018-08-31 without embargo terms The student, Kevin Meier, accepted the attached license on 2018-02-23 at 14:54. The student, Kevin Meier, submitted this Dissertation for approval on 2018-02-23 at 15:30. This Dissertation was approved for publication on 2018-02-28 at 14:33. DSpace SAF Submission Ingestion Package generated from Vireo submission #12047 on 2018-08-31 at 17:08:20 Made available in DSpace on 2018-09-04T20:26:36Z (GMT). No. of bitstreams: 5 MEIER-DISSERTATION-2018.pdf: 167308759 bytes, checksum: 68069e8459f3f91b33a127ed9bb81670 (MD5) DissertationCurveSLAM.mp4: 56811490 bytes, checksum: 4905ad4ed836db632f0a69939abf9dfc (MD5) DissertationShoreline.mp4: 61005697 bytes, checksum: 37cf9bf0809b2392ee4d65a94d6fc5b5 (MD5) LICENSE.txt: 4208 bytes, checksum: 11f78922e7a71e85dc7ff2bdafa6ef0d (MD5) PROQUEST_LICENSE.txt: 4554 bytes, checksum: 0720b788fda859df4e61f7f956a30a1f (MD5) Previous issue date: 2018-02-28 application/pdf http://hdl.handle.net/2142/100901 en Copyright 2018 Kevin C. Meier Visual-inertial SLAM River detection Visual-inertial curve SLAM text text Electrical & Computer Eng Electrical & Computer Engr University of Illinois at Urbana-Champaign Dissertation Ph.D.