  In this paper, we propose an online learning algorithm based on a
Rao-Blackwellized particle filter for spatial concept acquisition and mapping.
We have proposed a nonparametric Bayesian spatial concept acquisition model
(SpCoA). We propose a novel method (SpCoSLAM) integrating SpCoA and FastSLAM in
the theoretical framework of the Bayesian generative model. The proposed method
can simultaneously learn place categories and lexicons while incrementally
generating an environmental map. Furthermore, the proposed method has scene
image features and a language model added to SpCoA. In the experiments, we
tested online learning of spatial concepts and environmental maps in a novel
environment of which the robot did not have a map. Then, we evaluated the
results of online learning of spatial concepts and lexical acquisition. The
experimental results demonstrated that the robot was able to more accurately
learn the relationships between words and the place in the environmental map
incrementally by using the proposed method.
