Abstract

Stochastic topological models, and hidden Markov models in particular, are a useful tool for robotic navigation and planning. In previ- ous 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 head- ing of the robot result in large displacements in the odometric readings it maintains. The cumu- lative rotational error leads to unreliable odomet- ric 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.

Citations