Simultaneous planning localization and mapping: A hybrid Bayesian/frequentist approach Conference Paper

abstract

• In this paper, the problem of mapping and planning in an uncertain environment is studied. A hybrid Bayesian/ frequentist formulation of the simultaneous planning, localization and mapping (SPLAM) problem is presented wherein the environment is modeled as a stationary, spatially uncorrelated random process whose stationary probabilities are fixed but unknown, and have to be estimated as the autonomous system moves through the environment and makes observations using its sensors. The environmental random process is estimated using stochastic approximation algorithms. Under a certain "reliable sensor assumption", it is shown that the mapping algorithms converge with probability one, and that the convergence of the mapping algorithms is independent of the planning policy, as long as it is non-anticipative, akin to the celebrated "Separation Principle" in Classical Linear Control theory. Further, the computational burden of the mapping algorithms is significantly reduced when compared to Bayesian SPLAM techniques. 2008 AACC.

name of conference

• 2008 American Control Conference

authors

• Chakravorty, Suman

published proceedings

• 2008 AMERICAN CONTROL CONFERENCE, VOLS 1-12

author list (cited authors)

• Chakravorty, S., & Saha, R.

citation count

• 11

complete list of authors

• Chakravorty, S||Saha, R

publication date

• June 2008

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• Proceedings of the ... American Control Conference. American Control Conference  Journal

keywords

• 40 Engineering
• 4007 Control Engineering, Mechatronics And Robotics
• 49 Mathematical Sciences
• 4901 Applied Mathematics

Digital Object Identifier (DOI)

• 10.1109/ACC.2008.4586660

International Standard Book Number (ISBN) 13

• 978-1-4244-2078-0

start page

• 1226

end page

• +

URL

• http%3A%2F%2Fdx.doi.org%2F10.1109%2Facc.2008.4586660