# Collaborative SLAM based on Wifi Fingerprint Similarity and Motion Information

arXiv: https://arxiv.org/abs/2001.02759 · HTML: https://mlsanity.com/paper/2001.02759/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2019.

## Built on by (most cited first, 3 of 3)

- [WiFi Fingerprint Clustering for Urban Mobility Analysis](https://mlsanity.com/paper/2105.01274/) (2021)
- [A Kernel Method to Nonlinear Location Estimation with RSS-based Fingerprint](https://mlsanity.com/paper/2204.03724/) (2022)
- [When Simultaneous Localization and Mapping Meets Wireless Communications: A Survey](https://mlsanity.com/paper/2602.06995/) (2026)

## Abstract (arXiv)

Simultaneous localization and mapping (SLAM) has been extensively researched in past years particularly with regard to range-based or visual-based sensors. Instead of deploying dedicated devices that use visual features, it is more pragmatic to exploit the radio features to achieve this task, due to their ubiquitous nature and the widespread deployment of Wi-Fi wireless network. This paper presents a novel approach for collaborative simultaneous localization and radio fingerprint mapping (C-SLAM-RF) in large unknown indoor environments. The proposed system uses received signal strengths (RSS) from Wi-Fi access points (AP) in the existing infrastructure and pedestrian dead reckoning (PDR) from a smart phone, without a prior knowledge about map or distribution of AP in the environment. We claim a loop closure based on the similarity of the two radio fingerprints. To further improve the performance, we incorporate the turning motion and assign a small uncertainty value to a loop closure if a matched turning is identified. The experiment was done in an area of 130 meters by 70 meters and the results show that our proposed system is capable of estimating the tracks of four users with an accuracy of 0.6 meters with Tango-based PDR and 4.76 meters with a step counter-based PDR.