Inference in the space of topological maps: an MCMC-based approach

Abstract

While probabilistic techniques have been considered extensively in the context of metric maps, no general purpose probabilistic methods exist for topological maps. We present the concept of probabilistic topological maps (PTMs), a sample-based representation that approximates the posterior distribution over topologies given the available sensor measurements. The PTM is obtained through the use of MCMC-based Bayesian inference over the space of all possible topologies. It is shown that the space of all topologies is equivalent to the space of set partitions of all available measurements. While the space of possible topologies is intractably large, our use of Markov chain Monte Carlo sampling to infer the approximate histograms overcomes the combinatorial nature of this space and provides a general solution to the correspondence problem in the context of topological mapping. We present experimental results that validate our technique and generate good maps even when using only odometry as the sensor measurements.