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The m-space feature representation for slam
Massacusetts Institute of Technology, Cambridge, MA . (CAS/CVAP/CSC)ORCID iD: 0000-0002-7796-1438
KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS. KTH, School of Computer Science and Communication (CSC), Numerical Analysis and Computer Science, NADA.ORCID iD: 0000-0002-1170-7162
Georgia Institute of Tech- nology, Atlanta, GA.
2007 (English)In: IEEE Transactions on robotics, ISSN 1552-3098, E-ISSN 1941-0468, ISSN 1552-3098, Vol. 23, no 5, 1024-1035 p.Article in journal (Refereed) Published
Abstract [en]

In this paper, a new feature representation for simultaneous localization and mapping (SLAM) is discussed. The representation addresses feature symmetries and constraints explicitly to make the basic model numerically robust. In previous SLAM work, complete initialization of features is typically performed prior to introduction of a new feature into the map. This results in delayed use of new data. To allow early use of sensory data, the new feature representation addresses the use of features that initially have been partially observed. This is achieved by explicitly modelling the subspace of a feature that has been observed. In addition to accounting for the special properties of each feature type, the commonalities can be exploited in the new representation to create a feature framework that allows for interchanging of SLAM algorithms, sensor and features. Experimental results are presented using a low-cost Web-cam, a laser range scanner, and combinations thereof.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2007. Vol. 23, no 5, 1024-1035 p.
Keyword [en]
SLAM, Robot, Feature, Mapping, Localization
National Category
Robotics
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-183492DOI: 10.1109/TRO.2007.903807OAI: oai:DiVA.org:kth-183492DiVA: diva2:911764
Note

QC 20160315

Available from: 2016-03-14 Created: 2016-03-14 Last updated: 2017-11-30Bibliographically approved

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