# Online Spatial Concept and Lexical Acquisition with Simultaneous Localization and Mapping

## TL;DR

This paper introduces an online learning algorithm that enables robots to simultaneously learn spatial concepts, place categories, and lexical associations while incrementally mapping new environments using a Bayesian framework.

## Contribution

It presents a novel integration of spatial concept acquisition with SLAM, incorporating scene features and language models for improved incremental learning.

## Key findings

- Robots learned place categories and lexicons accurately in new environments.
- The method improved the relationship between words and environmental locations.
- Incremental mapping and lexical acquisition were demonstrated successfully.

## Abstract

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.