A probabilistic technique for simultaneous localization and door state estimation with mobile robots in dynamic environments
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.