  We propose a novel online learning algorithm, called SpCoSLAM 2.0, for
spatial concepts and lexical acquisition with high accuracy and scalability.
Previously, we proposed SpCoSLAM as an online learning algorithm based on
unsupervised Bayesian probabilistic model that integrates multimodal place
categorization, lexical acquisition, and SLAM. However, our original algorithm
had limited estimation accuracy owing to the influence of the early stages of
learning, and increased computational complexity with added training data.
Therefore, we introduce techniques such as fixed-lag rejuvenation to reduce the
calculation time while maintaining an accuracy higher than that of the original
algorithm. The results show that, in terms of estimation accuracy, the proposed
algorithm exceeds the original algorithm and is comparable to batch learning.
In addition, the calculation time of the proposed algorithm does not depend on
the amount of training data and becomes constant for each step of the scalable
algorithm. Our approach will contribute to the realization of long-term spatial
language interactions between humans and robots.
