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BIOLOGICALLY INSPIRED MOBILE ROBOT VISION LOCALIZATION
by
Christian Siagian
A Dissertation Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(COMPUTER SCIENCE)
August 2009
Copyright 2009 Christian Siagian
Object Description
| Title | Biologically inspired mobile robot vision localization |
| Author | Siagian, Christian |
| Author email | siagian@usc.edu; siagian@usc.edu |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Computer Science (Robotics & Automation) |
| School | Viterbi School of Engineering |
| Date defended/completed | 2009-05-04 |
| Date submitted | 2009 |
| Restricted until | Unrestricted |
| Date published | 2009-08-06 |
| Advisor (committee chair) | Itti, Laurent |
| Advisor (committee member) |
Biederman, Irving Nevatia, Ramakant Sukhatme, Gaurav Tjan, Bosco |
| Abstract | The problem of localization is central to endowing mobile machines with intelligence. Vision is a promising research path because of its versatility and robustness in most unconstrained environments, both indoors and outdoors. Today, with many available studies in human vision, there is a unique opportunity to develop systems that take inspiration from neuroscience. In this work we examine several important issues on how the human brain deal with vision in general, and localization in particular.; For one, the human visual system extracts a plethora of features from different domains (for example: colors, orientations, intensity). Each of them brings a different perspective in scene understanding and allows humans to localize in many types of environment. Furthermore, the human brain also introduces multiple scene abstractions that complement each other. Here, we focus on the gist model, which rapidly summarizes a scene (general semantic classifications, spatial layout, etc.), and saliency model, which guides visual attention to specific conspicuous regions within the field of view.; One hallmark biological characteristic that we rely upon is the utilization of coarse-to-fine paradigm. There are two parts in the system where this is clearly evident. One is in the multi-level localization module, where the system tries to interchangeably localize both to a general vicinity, and to a more accurate coordinate location. The second is in the process of recalling stored environment information through a form of guided (hierarchical) search using various contextual knowledge, which we believe is a key to its scalability.; In order to fairly assess our contributions, we test the system in three large scale outdoor environments - a building complex (126x180ft. area, 13966 testing images), a vegetation-filled park (270x360ft. area, 26397 testing images), and an open-field area (450x585ft. area, 34711 testing images) - each with its own challenges. We not only test its accuracy in terms of coordinate location, we also pay close attention to its efficiency in frame rate.; In the end, we describe the future directions of our research, such as how to go about inserting the localization module into a fully autonomous mobile robot system. |
| Keyword | vision localization; robot localization; saliency; gist; biologically-inspired vision |
| Language | English |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Provenance | Electronically uploaded by the author |
| Type | texts |
| Legacy record ID | usctheses-m2511 |
| Rights | Siagian, Christian |
| Repository name | Libraries, University of Southern California |
| Repository address | Los Angeles, California |
| Repository email | http://www.usc.edu/isd/libraries/services/ask_a_librarian/email/ |
| Filename | etd-Siagian-3154 |
| Archival file | uscthesesreloadpub_Volume40/etd-Siagian-3154.pdf |
Description
| Title | Page 1 |
| Full text | BIOLOGICALLY INSPIRED MOBILE ROBOT VISION LOCALIZATION by Christian Siagian A Dissertation Presented to the FACULTY OF THE GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (COMPUTER SCIENCE) August 2009 Copyright 2009 Christian Siagian |
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