Learning Occupancy Grids of Non-Stationary Objects with Mobile Robots
We propose an occupancy grid mapping algorithm for mobile robots operating in environments where objects may change their locations over time. Most mapping algorithms rely on a static world assumption, which cannot model non-stationary objects (chairs, desks,...). This paper describes an extension to the well-known occupancy grid mapping technique [5,10] for learning models of non-stationary objects. Our approach uses a map differencing technique to extract snapshots of non-stationary objects. It then employs the expectation maximization (EM) algorithm to learn models of these objects, and to solve the data association problem that arises when objects are seen at different places at different points in time. A Bayesian version of Occam’s razor is applied to determine the number of different objects in the model. Experimental results obtained in two different indoor environments illustrate that our approach robustly solves the data association problem, and generates accurate models of environments with non-stationary objects.