SBIR Phase I: Mobile Indoor Localization and Navigation System Using Sensory Data with Data Mining and Machine Learning Techniques

This Small Business Innovation Research (SBIR) Phase I project will explore the feasibility of an indoor localization technology that works efficiently and accurately when GPS does not. GPS receivers, although universally adopted by the general public, do not work indoors and suffer from inaccuracies of up to 25 meters in outdoor urban environments. By analyzing and processing data generated from WiFi, bluetooth, cellphone signals, magnetometers, accelerometers, compasses, and gyroscopes, a building's sensory blueprint can be created. The building's sensory blueprint can then be exploited to localize people holding smart mobile devices. The research will consist in investigating, designing, implementing, and validating the following: (i) a motion model capable of detecting a user's movement with accelerometers, compasses, and gyroscopes, (ii) a diverse set of different machine learning algorithms to be compared in terms of speed and localization accuracy, (iii) a probabilistic measurement model built from the best machine learning algorithm of phase (ii), and (iv) a Monte Carlo Localization (MCL) algorithm that combines the motion and measurement models. The final indoor localization algorithm will be implemented, tested in real-world conditions, and refined to prove the technology's superiority in terms of accuracy, speed, and applicability. The broader impact/commercial potential of this project is that it could revolutionize the way buildings are used. The technology offers benefits to both end-users and companies. On one hand, users inside large buildings (e.g., supermarkets, shopping malls, hospitals, museums) will have access to floor plans, location-based information, and turn-by-turn directions directly on their mobile devices. On the other hand, companies will be able to analyze their customers' movements and provide them with targeted information or advertising when and where they need it. Other applications of the technology will provide societal benefits: (i) first responders will be able to accurately localize victims thus reducing response times and saving lives, (ii) building managers will be able to save up to 30% energy and money by conditioning each room in real-time based on the room's occupancy, (iii) people with disabilities will be able to use the technology for assistance such as finding wheelchair-accessible routes, and (iv) warehouse managers will be able to reduce order fulfillment time. Indoor localization will be, in the near future, as pervasive as GPS is today.