# Extracting Semantic Indoor Maps from Occupancy Grids

arXiv: https://arxiv.org/abs/2002.08348 · HTML: https://mlsanity.com/paper/2002.08348/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Feb 2020.

## Built on by (most cited first, 2 of 2)

- [Room Classification on Floor Plan Graphs using Graph Neural Networks](https://mlsanity.com/paper/2108.05947/) (2021)
- [Structured Graph Network for Constrained Robot Crowd Navigation with Low Fidelity Simulation](https://mlsanity.com/paper/2405.16830/) (2024)

## Abstract (arXiv)

The primary challenge for any autonomous system operating in realistic, rather unconstrained scenarios is to manage the complexity and uncertainty of the real world. While it is unclear how exactly humans and other higher animals master these problems, it seems evident, that abstraction plays an important role. The use of abstract concepts allows to define the system behavior on higher levels. In this paper we focus on the semantic mapping of indoor environments. We propose a method to extract an abstracted floor plan from typical grid maps using Bayesian reasoning. The result of this procedure is a probabilistic generative model of the environment defined over abstract concepts. It is well suited for higher-level reasoning and communication purposes. We demonstrate the effectiveness of the approach using real-world data.