GIN - Global Indoor Navigation
| Reference number | |
| Coordinator | Combain Mobile AB |
| Funding from Vinnova | SEK 1 997 620 |
| Project duration | April 2020 - September 2021 |
| Status | Completed |
| Venture | Innovation projects in enterprises |
| Call | Innovation projects in small and medium-sized companies - autumn 2019 |
Important results from the project
Global Indoor Navigation (GIN) has aimed to develop a new self-learning indoor navigation that automatically characterizes buildings through the use of the service in ordinary mobile phones. New algorithms and methods have been developed that use collected position data and automatically identify the most common routes, stairs / elevators and entrances in a building. With this characterization you can then create a lot of valuable services e.g. indoor navigation, locating people in need, finding equipment and efficiency improvement solutions.
Expected long term effects
The project has developed a complete prototype for self-learning indoor positioning: 1. New Android app for data collection 2. New positioning methods with machine learning 3. New route extraction method that automatically calculates entrances and the most common paths in a building 4. Portal that shows calculated paths in a building and demonstrates indoor navigation Evaluation of different types of buildings shows that we can automatically estimate paths with approximately 2-10m accuracy, which is fully sufficient for indoor navigation in e.g. shopping center.
Approach and implementation
The project has been carried out by Combain together with the Centre for Mathematical Sciences, Lund University. The project has been based on self-learning indoor positioning, own IoT tracking platform and open source code. This made it possible to focus on new parts required and to be able to put together a prototype in a short time that demonstrates the automation from user data to a complete indoor navigation. The project has verified indoor navigation in four different types of buildings: housing, offices, universities and shopping centers. Larger buildings gave better results.