2026-09-20T11:44:12Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/99528 2023-07-11 col_2142_5131 col_2142_8888 com_2142_5130 com_2142_8887 com_2142_234

Embargo set by: Seth Robbins for item 105497 Lift date: 2020-03-13T17:36:05Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system Do, Minh Aranguiz, Dario 2018-03-13T17:35:54Z 2018-03-13T17:35:54Z 2020-03-14T09:15:25Z 2017-12-12 2017-12 This thesis provides an overview of the localization techniques used by the robotic platform Mousr, as well as the modeling assumptions and simulator scaffolding used to test these techniques. Mousr is a centimeter-scale differential drive robot with an IMU, wheel encoders, and a front-facing time-of-flight sensor. We fuse the differential IMU and wheel encoder measurements with the absolute time-of-flight sensor measurements via particle filter to produce an accurate localization estimate within a known map. Our particle filter successfully accounts for the nonlinearities of the time-of-flight sensor model; we are able to driftlessly drive around a known map in perpetuity. We also discuss a method to approximate the information gain of a map, a crucial component of active localization techniques. Our Jensen-based approximation runs over 20,000 times faster than the exact computation within a 2m x 2m region with minimal degradation in accuracy. Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2019-12-01 The student, Dario Aranguiz, accepted the attached license on 2017-12-12 at 09:32. The student, Dario Aranguiz, submitted this Thesis for approval on 2017-12-12 at 09:37. This Thesis was approved for publication on 2017-12-12 at 11:23. DSpace SAF Submission Ingestion Package generated from Vireo submission #11941 on 2018-03-13 at 10:38:08 Made available in DSpace on 2018-03-13T17:35:54Z (GMT). No. of bitstreams: 2 ARANGUIZ-THESIS-2017.pdf: 3896550 bytes, checksum: 4e25aaa8b0d563a7638139557ffaf2e1 (MD5) LICENSE.txt: 4211 bytes, checksum: a6d51519a1baea7b6978a8774b26afdd (MD5) Previous issue date: 2017-12-12 Limited Restriction Lifted for Item 105497 on 2020-03-14T09:15:25Z. application/pdf http://hdl.handle.net/2142/99528 en Copyright 2017 Dario Aranguiz Robotics Localization Particle filter Time of flight Low-cost time-of-flight-based localization techniques for robotic applications text text Electrical & Computer Eng Electrical & Computer Engr University of Illinois at Urbana-Champaign Thesis M.S.