# Topological Indoor Mapping through WiFi Signals

arXiv: https://arxiv.org/abs/2106.09789 · HTML: https://mlsanity.com/paper/2106.09789/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 31 references that are in the map.
- First posted on arXiv 17 Jun 2021.

## Builds on (most cited first, 7 of 7)

- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [A Survey of Indoor Localization Systems and Technologies](https://mlsanity.com/paper/1709.01015/) (2017)
- [Clustering with t-SNE, provably](https://mlsanity.com/paper/1706.02582/) (2017)
- [Heavy-tailed kernels reveal a finer cluster structure in t-SNE visualisations](https://mlsanity.com/paper/1902.05804/) (2019)
- [HypTrails: A Bayesian Approach for Comparing Hypotheses About Human Trails on the Web](https://mlsanity.com/paper/1411.2844/) (2014)
- [Topic Space Trajectories: A case study on machine learning literature](https://mlsanity.com/paper/2010.12294/) (2020)
- [Distances for WiFi Based Topological Indoor Mapping](https://mlsanity.com/paper/1809.07405/) (2018)

## Abstract (arXiv)

The ubiquitous presence of WiFi access points and mobile devices capable of measuring WiFi signal strengths allow for real-world applications in indoor localization and mapping. In particular, no additional infrastructure is required. Previous approaches in this field were, however, often hindered by problems such as effortful map-building processes, changing environments and hardware differences. We tackle these problems focussing on topological maps. These represent discrete locations, such as rooms, and their relations, e.g., distances and transition frequencies. In our unsupervised method, we employ WiFi signal strength distributions, dimension reduction and clustering. It can be used in settings where users carry mobile devices and follow their normal routine. We aim for applications in short-lived indoor events such as conferences.