Learning Topological Maps with Weak Local Odometric Information

cal maps provide a useful abstraction for robotic navigation and planning. Although stochastic mapscan theoreticallybe learned using the Baum-Welch algorithm,without strong prior constraint on the structure of the model it is slow to converge, requires a great deal of data, and is often stuck in local minima. In this paper, we consider a special case of hidden Markov models for robot-navigation environments, in which states are associated with points in a metric con guration space. We assume that the robot has some odometric ability to measure relative transformationsbetween its con gurations. Such odometry is typically not precise enough to su ce for building a global map,but it does givevaluablelocalinformation about relations between adjacent states. We present an extension of the Baum-Welch algorithm that takes advantage of this local odometric information,yielding faster convergence to better solutions with less data.

• Type: Conference
• Year: 1997
• Where: IJCAI
• Authors: Hagit Shatkay, Leslie Pack Kaelbling
• Keywords: Hidden Markov Models, IJCAI 1997, IJCAI 2007, Stochastic Mapscan Theoreticallybe, Strong Prior Constraint