Graphical SLAM using vision and the measurement subspace
2005 (English)In: 2005 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS, VOLS 1-4, IEEE conference proceedings, 2005, 325-330 p.Conference paper (Refereed)
In this paper we combine a graphical approach for simultaneous localization and mapping, SLAM, with a feature representation that addresses symmetries and constraints in the feature coordinates, the measurement subspace, M-space. The graphical method has the advantages of delayed linearizations and soft commitment to feature measurement matching. It also allows large maps to be built up as a network of small local patches, star nodes. This local map net is then easier to work with. The formation of the star nodes is explicitly stable and invariant with all the symmetries of the original measurements. All linearization errors are kept small by using a local frame. The construction of this invariant star is made clearer by the M-space feature representation. The M-space allows the symmetries and constraints of the measurements to be explicitly represented. We present results using both vision and laser sensors.
Place, publisher, year, edition, pages
IEEE conference proceedings, 2005. 325-330 p.
SLAM, vision, graph, features
Computer Vision and Robotics (Autonomous Systems)
IdentifiersURN: urn:nbn:se:kth:diva-38228DOI: 10.1109/IROS.2005.1545493ISI: 000235632103049ScopusID: 2-s2.0-79957980708ISBN: 0-7803-8912-3OAI: oai:DiVA.org:kth-38228DiVA: diva2:436290
IEEE/RSJ International Conference on Intelligent Robots and Systems. Edmonton, CANADA. AUG 02-06, 2005
QC 201110102012-11-162011-08-232012-11-16Bibliographically approved