# Incremental Semantic Mapping with Unsupervised On-line Learning

arXiv: https://arxiv.org/abs/1907.04001 · HTML: https://mlsanity.com/paper/1907.04001/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2019.

## Built on by (most cited first, 1 of 1)

- [Topological Semantic Mapping by Consolidation of Deep Visual Features](https://mlsanity.com/paper/2106.12709/) (2021)

## Abstract (arXiv)

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.