  Nowadays, mobile robots are deployed in many indoor environments, such as
offices or hospitals. These environments are subject to changes in the
traversability that often happen by following repeating patterns. In this
paper, we investigate the problem of navigating in such environments over
extended periods of time by capturing these patterns and exploiting this
knowledge to make informed decisions. Our approach incrementally estimates a
model of the traversability changes from robot's observations and uses a
probabilistic graphical model to make predictions at currently unobserved
locations. In the belief space defined by the predictions, we plan paths that
trade off the risk to encounter obstacles and the information gain of visiting
unknown locations. We implemented our approach and tested it in different
indoor environments. The experiments suggest that in the long run, our approach
leads to navigation along shorter paths compared to following a greedy shortest
path policy.
