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Author Avots, Dzintars ♦ Lim, Edward ♦ Thibaux, Romain ♦ Thrun, Sebastian
Source CiteSeerX
Content type Text
File Format PDF
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Mobile Robot ♦ Particle Filter ♦ Mobile Robot Localization Technique ♦ Conditional Binary Bayes Filter ♦ Current Localization Technique ♦ Simultaneous Localization ♦ Estimation Problem ♦ Probabilistic Technique ♦ Static Map ♦ Robot Localization ♦ Mixed Discrete-continuous State Estimation ♦ Dynamic Environment ♦ Door State Estimation ♦ State-of-the-art Localizer ♦ Dynamic State ♦ Experimental Result ♦ Factored Estimation Algorithm ♦ Environment Change
Description Virtually all existing mobile robot localization techniques operate on a static map of the environment. When the environment changes (e.g., doors are opened or closed), there is an opportunity to simultaneously estimate the robot's pose and the state of the environment. The resulting estimation problem is high-dimensional, rendering current localization techniques inapplicable. This paper proposes an efficient, factored estimation algorithm for mixed discrete-continuous state estimation. Our algorithm integrates particle filters for robot localization, and conditional binary Bayes filters for estimating the dynamic state of the environment. Experimental results illustrate that our algorithm is highly effective in estimating the status of doors, and outperforms a state-of-the-art localizer in dynamic environments.
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Date 2002-01-01
Publisher Institution In IROS-2002