  This paper introduces an incremental semantic mapping approach, with on-line
unsupervised learning, based on Self-Organizing Maps (SOM) for robotic agents.
The method includes a mapping module, which incrementally creates a topological
map of the environment, enriched with objects recognized around each
topological node, and a module of places categorization, endowed with an
incremental unsupervised learning SOM with on-line training. The proposed
approach was tested in experiments with real-world data, in which it
demonstrates promising capabilities of incremental acquisition of topological
maps enriched with semantic information, and for clustering together similar
places based on this information. The approach was also able to continue
learning from newly visited environments without degrading the information
previously learned.
