Simultaneous Localization and Mapping in Dense Environments Conference Paper

abstract

• A hybrid Bayesian/ frequentist approach is presented for the Simultaneous Localization and Mapping Problem (SLAM). A frequentist approach is proposed for mapping with time varying robotic poses and is generalized to the case when the robotic pose is both time varying and uncertain. The SLAM problem is then solved in two steps: 1) the robot is localized with respect to a sparse set of landmarks in the map using a Bayes filter and a belief on the robot pose is formed, and 2) this belief on the robot pose is used to map the rest of the map using the frequentist estimator. The hybrid methodology is shown to have complexity linear in the map components, is robust to the data association problem and is provably consistent. 2009 IEEE.

name of conference

• 2009 IEEE International Conference on Systems, Man and Cybernetics

authors

• Chakravorty, Suman

published proceedings

• 2009 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC 2009), VOLS 1-9

author list (cited authors)

• Chakravorty, S., & Saha, R.

citation count

• 0

complete list of authors

• Chakravorty, S||Saha, R

publication date

• October 2009

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• Conference proceedings / IEEE International Conference on Systems, Man, and Cybernetics. IEEE International Conference on Systems, Man, and Cybernetics  Journal

keywords

• 40 Engineering
• 4007 Control Engineering, Mechatronics And Robotics
• 46 Information And Computing Sciences
• 4602 Artificial Intelligence
• Bioengineering

Digital Object Identifier (DOI)

• 10.1109/ICSMC.2009.5346573

International Standard Book Number (ISBN) 13

• 978-1-4244-2793-2

start page

• 2769

end page

• 2774

URL

• http://dx.doi.org/10.1109/icsmc.2009.5346573