{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/91695"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/91695","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"A generalized (k, m)-segment mean algorithm for long term modeling of traversable environments","abstract":"We present an ecient algorithm for computing semantic environment models and activity patterns in terms of those models from long-term value trajectories defined as sensor data streams. We use an expectation-maximization approach to calculate a locally optimal set of path segments with minimal total error from the given data signal. This process reduces the raw data stream to an approximate semantic representation. The algorithm's speed is greatly improved by the use of lossless coresets during the iterative update step, as they can be calculated in constant amortized time to perform operations with otherwise linear runtimes. We evaluate the algorithm for two types of data, GPS points and video feature vectors, on several data sets collected from robots and human-directed agents. These experiments demonstrate the algorithm's ability to reliably and quickly produce a model which closely ts its input data, at a speed which is empirically no more than linear relative to the size of that data set. We analyze several topological maps and representative feature sets produced from these data sets.","abstract_html":"We present an ecient algorithm for computing semantic environment models and activity patterns in terms of those models from long-term value trajectories defined as sensor data streams. We use an expectation-maximization approach to calculate a locally optimal set of path segments with minimal total error from the given data signal. This process reduces the raw data stream to an approximate semantic representation. The algorithm's speed is greatly improved by the use of lossless coresets during the iterative update step, as they can be calculated in constant amortized time to perform operations with otherwise linear runtimes. We evaluate the algorithm for two types of data, GPS points and video feature vectors, on several data sets collected from robots and human-directed agents. These experiments demonstrate the algorithm's ability to reliably and quickly produce a model which closely ts its input data, at a speed which is empirically no more than linear relative to the size of that data set. We analyze several topological maps and representative feature sets produced from these data sets.","abstract_has_math":false,"creators":["Layton, Todd Samuel"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.","school":null,"contributors":[],"advisors":["Daniela Rus."],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014","date_published":"2014","updated_at":"2026-07-22T22:21:34Z","subjects":["Electrical Engineering and Computer Science."],"languages":["eng"],"rights":["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."],"rights_urls":["http://dspace.mit.edu/handle/1721.1/7582"],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/1721.1/91695","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Daniela Rus."]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."]},{"key":"dc:contributor.other","label":"Dc Contributor Other","values":["Massachusetts Institute of Technology. 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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."]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://dspace.mit.edu/handle/1721.1/7582"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/1721.1/91695"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.","This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.","Cataloged from student-submitted PDF version of thesis.","Includes bibliographical references (pages 75-76)."]},{"key":"dc:description.abstract","label":"Abstract","values":["We present an ecient algorithm for computing semantic environment models and activity patterns in terms of those models from long-term value trajectories defined as sensor data streams. We use an expectation-maximization approach to calculate a locally optimal set of path segments with minimal total error from the given data signal. This process reduces the raw data stream to an approximate semantic representation. The algorithm's speed is greatly improved by the use of lossless coresets during the iterative update step, as they can be calculated in constant amortized time to perform operations with otherwise linear runtimes. We evaluate the algorithm for two types of data, GPS points and video feature vectors, on several data sets collected from robots and human-directed agents. These experiments demonstrate the algorithm's ability to reliably and quickly produce a model which closely ts its input data, at a speed which is empirically no more than linear relative to the size of that data set. We analyze several topological maps and representative feature sets produced from these data sets."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M. Eng."]},{"key":"dc:title","label":"Title","values":["A generalized (k, m)-segment mean algorithm for long term modeling of traversable environments"]}]}],"canonical_facts":{"dc:contributor.advisor":["Daniela Rus."],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."],"dc:contributor.other":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."],"dc:creator":["Layton, Todd Samuel"],"dc:date.accessioned":["2014-11-24T16:16:11Z"],"dc:date.available":["2014-11-24T16:16:11Z"],"dc:date.issued":["2014"],"dc:description":["Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.","This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.","Cataloged from student-submitted PDF version of thesis.","Includes bibliographical references (pages 75-76)."],"dc:description.abstract":["We present an ecient algorithm for computing semantic environment models and activity patterns in terms of those models from long-term value trajectories defined as sensor data streams. We use an expectation-maximization approach to calculate a locally optimal set of path segments with minimal total error from the given data signal. This process reduces the raw data stream to an approximate semantic representation. The algorithm's speed is greatly improved by the use of lossless coresets during the iterative update step, as they can be calculated in constant amortized time to perform operations with otherwise linear runtimes. We evaluate the algorithm for two types of data, GPS points and video feature vectors, on several data sets collected from robots and human-directed agents. These experiments demonstrate the algorithm's ability to reliably and quickly produce a model which closely ts its input data, at a speed which is empirically no more than linear relative to the size of that data set. We analyze several topological maps and representative feature sets produced from these data sets."],"dc:description.degree":["M. 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