Crowdsourcing A Practical Indoor GPS

Scientists in South Korea have developed an indoor locating technology using crowdsourced fingerprints from smartphones.

AsianScientist (Apr. 11, 2017) – Researchers at the Korea Advanced Institute of Science and Technology (KAIST) have tapped smartphones to significantly reduce the cost of constructing an indoor localization system while maintaining high accuracy. Their findings have been published in IEEE Transactions on Mobile Computing.

The method can be used in any building in the world, provided the floor plan is available and there are Wi-Fi fingerprints to collect.

“This technology allows the easy deployment of highly accurate indoor localization systems in any building in the world. In the near future, most indoor spaces will be able to provide localization services, just like outdoor spaces,” said study author Professor Han Dong-Soo.

The global positioning system (GPS), which is now an indispensable part of everyday navigation, does not work indoors and fares poorly in two dimensions.

Years ago, the team developed a method to automatically label resting space locations from signals collected in various contexts such as homes, shops, and offices via the users’ home or office address information. The latest method allows for the automatic labelling of transient space locations such as hallways, lobbies, and stairs using unsupervised learning, without any additional location information.

To accurately collect and label the location information of the Wi-Fi fingerprints, the research team analyzed indoor space utilization. This led to technology that classified indoor spaces into places used for stationary tasks (resting spaces) and spaces used to reach said places (transient spaces), and utilized separate algorithms to optimally and automatically collect location labelling data.

Tests showed that the technology is capable of accuracy up to three or four meters given enough training data. The accuracy level is comparable to technology using manually-labeled location information.

Smartphone-collected Wi-Fi fingerprints have thus far been unutilized, Han added, but should be treated as invaluable resources.

This new indoor navigation technology is likely to be valuable to Google, Apple, or other global firms providing indoor positioning services globally. The technology will also be valuable for helping domestic firms provide positioning services.

“The new global indoor localization system deployment technology will be added to KAILOS, KAIST’s indoor localization system,” Han said.

KAILOS was released in 2014 as KAIST’s open platform for indoor localization service, allowing anyone in the world to add floor plans to KAILOS, and collect the building’s Wi-Fi fingerprints for a universal indoor localization service.

The article can be found at: Jung et al. (2016) Unsupervised Learning for Crowdsourced Indoor Localization in Wireless Networks.


Source: Korea Advanced Institute of Science and Technology.
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