Machine Learning for Smart Indoor NavigationSpeaker: Sudeep Pasricha – Fort Collins, CO, United States
Topic(s): Architecture, Embedded Systems and Electronics, Robotics
AbstractThe advent of the Global Positioning System (GPS) transformed the global transportation industry and allowed vehicles to not only localize themselves but also to navigate reliably and in a secure manner across the world at high speeds. Today, indoor localization is poised to reinvent the way we navigate within buildings and subterranean locales, with many benefits, e.g., directing emergency response services after a 911 call to a precise location (with sub-meter accuracy) inside a building, accurate tracking of equipment and inventory in hospitals, factories, and warehouses, etc. As GPS signals are severely attenuated or totally blocked in indoor environments, very different solutions are needed to support localization and navigation. The most effective approaches rely on wireless signals and exploit principles of proximity, trilateration, triangulation, and fingerprinting. In this talk I will present an overview of the most promising approaches for indoor navigation with smartphones and Internet of Things (IoT) devices. I will then discuss the many challenges related to short-term and long-term accuracy, device heterogeneity, battery lifetime, security, and noise resilience that are crucial to address in any indoor navigation solution. Next, I will present a machine learning driven framework that addresses many of the key challenges towards realizing a viable indoor localization solution with smart mobile devices. Lastly, I will discuss open challenges and new directions of importance as we move towards a future with seamless indoor navigation that promises to significantly enhance our quality of life.
About this LectureNumber of Slides: 60
Duration: 50 minutes
Languages Available: English
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