Crowdsourcing-Based Global Indoor Positioning System


Research team of Professor Dong-Soo Han of the School of Computing Intelligent Service Lab at KAIST developed a system for providing global indoor localization using Wi-Fi signals. The technology uses numerous smartphones to collect fingerprints of location data and label them automatically, significantly reducing the cost of constructing an indoor localization system while maintaining high accuracy.

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. 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.

Years ago, the team implemented a way 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. Testing in KAIST’s N5 building and the 7th floor of N1 building manifested 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.

Google, Microsoft, and other multinational corporations collected tens of thousands of floor plans for their indoor localization projects. Indoor radio map construction was also attempted by the firms but proved more difficult. As a result, existing indoor localization services were often plagued by inaccuracies. In Korea, COEX, Lotte World Tower, and other landmarks provide comparatively accurate indoor localization, but most buildings suffer from the lack of radio maps, preventing indoor localization services.

Professor Han said, “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.” He further added that smartphone-collected Wi-Fi fingerprints have been unutilized and often discarded, but now they should be treated as invaluable resources, which create a new big data field of Wi-Fi fingerprints. 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.

Professor Han added that “the new global indoor localization system deployment technology will be added to KAILOS, KAIST’s indoor localization system.” 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. As localization accuracy improves in indoor environments, despite the absence of GPS signals, applications such as location-based SNS, location-based IoT, and location-based O2O are expected to take off, leading to various improvements in convenience and safety. Integrated indoor-outdoor navigation services are also visible on the horizon, fusing vehicular navigation technology with indoor navigation.

Professor Han’s research was published in IEEE Transactions on Mobile Computing (TMC) in November in 2016.