Access Restriction

Author Biswas, Rahul ♦ Limketkai, Benson ♦ Sanner, Scott ♦ Thrun, Sebastian
Source CiteSeerX
Content type Text
File Format PDF
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Mobile Robot ♦ Natural Goal ♦ Bayesian Complexity Measure ♦ Nonstationary Object ♦ Towards Object Mapping ♦ Single Map ♦ Straightforward Map ♦ Occupancy Grid Mapping Algorithm ♦ Object Model ♦ Different Object ♦ Non-stationary Environment ♦ Static World Assumption ♦ Individual Occupancy Grid Map ♦ Well-known Occupancy Grid Mapping Technique ♦ Robotics Research ♦ Non-stationary Object ♦ Environment Mapping Algorithm ♦ Expectation Maximization Algorithm ♦ Different Point ♦ Multiple Map
Description We propose an occupancy grid mapping algorithm for mobile robots operating in environments where objects change their locations over time. Virtually all existing environment mapping algorithms rely on a static world assumption, rendering them inapplicable to environments where things (chairs, desks,...) move. A natural goal of robotics research, thus, is to learn models of nonstationary objects, and determine where they are at any point in time. This paper proposes an extension to the well-known occupancy grid mapping technique. Our approach uses a straightforward map differencing technique to detect changes in an environment over time. It employs the expectation maximization algorithm to learn models of non-stationary objects, and to determine the location of such objects in individual occupancy grid maps built at different points in time. By combining data from multiple maps when learning object models, the resulting models have higher fidelity than could be obtained from any single map. A Bayesian complexity measure is applied to determine the number of different objects in the model, making it possible to apply the approach to situations where not all objects are present at all times in the map. 1
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Date 2002-01-01
Publisher Institution In IEEE/RSJ Int. Conf on Intelligent Robots and Systems