Heading in the right direction

Stochastic topological models, and hidden Markov models in particular, are a useful tool for robotic navigation and planning. In previous work we have shown how weak odometric data can be used to improve learning topological models, overcoming the common problems of the standard Baum-Welch algorithm. Odometric data typically contain directional information, which imposes two difficulties: First, the cyclicity of the data requires the use of special circular distributions. Second, small errors in the heading of the robot result in large displacements in the odometric readings it maintains. The cumulative rotational error leads to unreliable odometric readings. In the paper we present solutions to these problems by using a circular distribution and relative coordinate systems. We validate their effectiveness through experimental results from a model-learning application.