Long-Term Robot Navigation in Indoor Environments Estimating Patterns in Traversability Changes
This addresses the challenge of efficient navigation for robots in dynamic indoor settings like offices or hospitals, though it is incremental as it builds on existing probabilistic modeling and planning methods.
The paper tackles the problem of long-term robot navigation in indoor environments by estimating patterns in traversability changes, resulting in navigation along shorter paths compared to a greedy shortest path policy.
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.