# Incremental Semantic Mapping with Unsupervised On-line Learning

## TL;DR

This paper presents an incremental semantic mapping method using unsupervised online learning with Self-Organizing Maps, enabling robots to build topological maps enriched with semantic data and adapt to new environments without forgetting prior knowledge.

## Contribution

It introduces a novel approach combining topological mapping and unsupervised online learning with SOMs for semantic place categorization in robotics.

## Key findings

- Successfully demonstrated in real-world experiments
- Enables continuous learning without degrading previous knowledge
- Effectively clusters similar places based on semantic information

## Abstract

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.