Dynamic visual understanding of the local environment for an indoor navigating robot

We present a method for an embodied agent with vision sensor to create a concise and useful model of the local indoor environment from its experience of moving within it. Our method generates and evaluates a set of qualitatively distinct hypotheses of the local environment and refines the parameters within each hypothesis quantitatively. Our method is a continual, incremental process that transforms current environmental-structure hypotheses into children hypotheses describing the same environment in more detail. Since our method only relies on simple geometric and probabilistic inferences, our method runs in real-time, and it avoids the need of extensive prior training and the Manhattan-world assumption, which makes it practical and efficient for a navigating robot. Experimental results on a collection of indoor videos suggests that our method is capable of modeling various structures of indoor environments.