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

arXiv: https://arxiv.org/abs/1704.04664 · HTML: https://mlsanity.com/paper/1704.04664/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Apr 2017.

## Built on by (most cited first, 10 of 10)

- [Multiagent Multimodal Categorization for Symbol Emergence: Emergent Communication via Interpersonal Cross-modal Inference](https://mlsanity.com/paper/2109.07194/) (2021)
- [Double Articulation Analyzer with Prosody for Unsupervised Word and Phoneme Discovery](https://mlsanity.com/paper/2103.08199/) (2021)
- [Hippocampal formation-inspired probabilistic generative model](https://mlsanity.com/paper/2103.07356/) (2021)
- [A Whole Brain Probabilistic Generative Model: Toward Realizing Cognitive Architectures for Developmental Robots](https://mlsanity.com/paper/2103.08183/) (2021)
- [Unsupervised Lexical Acquisition of Relative Spatial Concepts Using Spoken User Utterances](https://mlsanity.com/paper/2106.08574/) (2021)
- [Active Exploration based on Information Gain by Particle Filter for Efficient Spatial Concept Formation](https://mlsanity.com/paper/2211.10934/) (2022)
- [Hierarchical Bayesian Model for the Transfer of Knowledge on Spatial Concepts based on Multimodal Information](https://mlsanity.com/paper/2103.06442/) (2021)
- [Hierarchical Path-planning from Speech Instructions with Spatial Concept-based Topometric Semantic Mapping](https://mlsanity.com/paper/2203.10820/) (2022)
- [Online-Score-Aided Federated Learning for Resource-Constrained Wireless Clients with Continual Data Arrival](https://mlsanity.com/paper/2408.05886/) (2024)
- [Symbol emergence as interpersonal cross-situational learning: the emergence of lexical knowledge with combinatoriality](https://mlsanity.com/paper/2306.15837/) (2023)

## Abstract (arXiv)

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.