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dc.contributor.authorSteiner, Ted J
dc.contributor.authorHuang, Guoquan
dc.contributor.authorLeonard, John J
dc.date.accessioned2017-03-13T16:27:54Z
dc.date.available2017-03-13T16:27:54Z
dc.date.issued2015-07
dc.date.submitted2015-05
dc.identifier.isbn978-1-4799-6923-4
dc.identifier.isbn978-1-4799-6924-1
dc.identifier.isbn978-1-4799-6922-7
dc.identifier.otherINSPEC Accession Number: 15286382
dc.identifier.urihttp://hdl.handle.net/1721.1/107401
dc.description.abstractMaps used for navigation often include a database of location descriptions for place recognition (loop closing), which permits bounded-error performance. A standard pose-graph SLAM system adds a new entry for every new pose into the location database, which grows linearly and unbounded in time and thus becomes unsustainable. To address this issue, in this paper we propose a new map-reduction approach that pre-constructs a fixed-size place-recognition database amenable to the limited storage and processing resources of the vehicle by exploiting the high-level structure of the environment as well as the vehicle motion. In particular, we introduce the concept of location utility - which encapsulates the visitation probability of a location and its spatial distribution relative to nearby locations in the database - as a measure of the value of potential loop-closure events to occur at that location. While finding the optimal reduced location database is NP-hard, we develop an efficient greedy algorithm to sort all the locations in a map based on their relative utility without access to sensor measurements or the vehicle trajectory. This enables pre-determination of a generic, limited-size place-recognition database containing the N best locations in the environment. To validate the proposed approach, we develop an open-source street-map simulator using real city-map data and show that an accurate map (pose-graph) can be attained even when using a place-recognition database with only 1% of the entries of the corresponding full database.en_US
dc.description.sponsorshipCharles Stark Draper Laboratory (Fellowship)en_US
dc.language.isoen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/ICRA.2015.7139223en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourceMIT Web Domainen_US
dc.titleLocation utility-based map reductionen_US
dc.typeArticleen_US
dc.identifier.citationSteiner, Ted J., Guoquan Huang, and John J. Leonard. “Location Utility-Based Map Reduction.” 2015 IEEE International Conference on Robotics and Automation (ICRA) (May 2015).en_US
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratoryen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Aeronautics and Astronauticsen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Mechanical Engineeringen_US
dc.contributor.mitauthorSteiner, Ted J
dc.contributor.mitauthorHuang, Guoquan
dc.contributor.mitauthorLeonard, John J
dc.relation.journalProceeding of the 2015 IEEE International Conference on Robotics and Automation (ICRA)en_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dspace.orderedauthorsSteiner, Ted J.; Huang, Guoquan; Leonard, John J.en_US
dspace.embargo.termsNen_US
dc.identifier.orcidhttps://orcid.org/0000-0002-8863-6550
mit.licenseOPEN_ACCESS_POLICYen_US
mit.metadata.statusComplete


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