2026-09-19T16:52:40Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/978132026-06-16T18:53:24Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022
Nicholas Roy.
Velez, Javier J
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2015-07-17T19:49:15Z
2015-07-17T19:49:15Z
2015
2015
http://hdl.handle.net/1721.1/97813
912401438
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 127-134).
In this thesis we explore models and algorithms used by an autonomous agent to find objects in the real world. We begin by tackling the problem of determining the existence and location of an object robustly given the sensors employed on an agent. Our major contribution lies in modeling the spatial correlations between the sensor and object. Next, we develop models and algorithms used to explore the world in order to find all of the objects. We develop a model with tractable inference which reasons about the locations of all the objects, seen and unseen, by reformulating our problem into one of assigning objects to particular clusters. Along the way we analyze theoretical properties relating to the number of un-informative decision any agent must make in order to find all the objects in the world using the theory of random graphs, particularly percolation theory. The developed systems improve upon the state-of-the art in both simulation and real-world experiments.
by Javier J. Velez.
Ph.D.
134 pages
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Robust object exploration and detection
Thesis
application/pdf
false
Publication
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="248f4b6f-7b84-4ef5-8d2a-9365312ed15b">
<Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type>
<Language>eng</Language>
<Title>Robust object exploration and detection</Title>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2015</PublicationDate>
<Authors>
<Author>
<DisplayName>Velez, Javier J</DisplayName>
<Affiliation>
<OrgUnit>
</OrgUnit>
</Affiliation>
</Author>
</Authors>
<Editors>
</Editors>
<Publishers>
<Publisher>
<DisplayName>Massachusetts Institute of Technology</DisplayName>
<OrgUnit />
</Publisher>
</Publishers>
<License>http://dspace.mit.edu/handle/1721.1/7582</License>
<Keyword>Electrical Engineering and Computer Science.</Keyword>
<Abstract>In this thesis we explore models and algorithms used by an autonomous agent to find objects in the real world. We begin by tackling the problem of determining the existence and location of an object robustly given the sensors employed on an agent. Our major contribution lies in modeling the spatial correlations between the sensor and object. Next, we develop models and algorithms used to explore the world in order to find all of the objects. We develop a model with tractable inference which reasons about the locations of all the objects, seen and unseen, by reformulating our problem into one of assigning objects to particular clusters. Along the way we analyze theoretical properties relating to the number of un-informative decision any agent must make in order to find all the objects in the world using the theory of random graphs, particularly percolation theory. The developed systems improve upon the state-of-the art in both simulation and real-world experiments.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>