Graphical SLAM: a self-correcting map
2004 (English)In: 2004 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION, PROCEEDINGS , 2004, 383-390 p.Conference paper (Refereed)
We describe an approach to simultaneous localization and mapping, SLAM. This approach has the highly desirable property of robustness to data association errors. Another important advantage of our algorithm is that non-linearities are computed exactly, so that global constraints can be imposed even if they result in large shifts to the map. We represent the map as a graph and use the graph to find an efficient map update algorithm. We also show how topological consistency can be imposed on the map, such as, closing a loop. The algorithm has been implemented on an outdoor robot and we have experimental validation of our ideas. We also explain how the graph can be simplified leading to linear approximations of sections of the map. This reduction gives us a natural way to connect local map patches into a much larger global map.
Place, publisher, year, edition, pages
2004. 383-390 p.
, IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION, ISSN 1050-4729 ; 2004:1
Algorithms, Eigenvalues and eigenfunctions, Kalman filtering, Lagrange multipliers, Matrix algebra, Probability, Real time systems, Sensors, Topology, Data association errors, Global consistence constraints, Mahalanobis distance, Simultaneous localization and mapping (SLAM)
Engineering and Technology
IdentifiersURN: urn:nbn:se:kth:diva-38224DOI: 10.1109/ROBOT.2004.1307180ISI: 000221794800061ScopusID: 2-s2.0-3042628835ISBN: 0-7803-8232-3OAI: oai:DiVA.org:kth-38224DiVA: diva2:436286
IEEE International Conference on Robotics and Automation, New Orleans, LA, APR 26-MAY 01, 2004
QC 201108252011-08-232011-08-232012-01-18Bibliographically approved