Combination Of Real And Virtual World For Indoor Navigation using Mobile Application
Vidhyavani.A1, StephinStanly2, Ankit Kumar Pandey3, Shivam Choudhury4
1Ms.Vidhyavani.A, Department of CSE, SRMIST, Ramapuram Campus, Chennai (Tamil Nadu). India.
2Stephin Stanly, Department of CSE, SRMIST, Ramapuram Campus, Chennai (Tamil Nadu). India.
3Ankit Kumar Pandey, Department of CSE, SRMIST, Ramapuram Campus, Chennai (Tamil Nadu). India.
4Shivam Choudhury, Department of CSE, SRMIST, Ramapuram Campus, Chennai (Tamil Nadu). India.
Manuscript received on 18 April 2019 | Revised Manuscript received on 25 April 2019 | Manuscript published on 30 April 2019 | PP: 307-310 | Volume-8 Issue-4, April 2019 | Retrieval Number: D6099048419/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In today’s life when the cities are rising usage areas of mobile phones have increased in the last 10 years. Although there have been improvements in many areas, most of the developments are in the field of positioning systems. Although the people’s lives continue in indoor environments, location-based information system receives data from the satellites, which can detect a person’s location in outdoor areas alone. But for indoor areas no efficient and perfect technology has been developed for the navigation or positioning. In this paper we have come up with an exceptional solution that is indoor navigation mobileapplication which works with augmented reality, in our proposed solution, we will be using mobile camera as the scanner for getting the path and extracting the features from various objects in the path. We use AR Core SDK which is the heart of this project which has an inbuilt property called area learning which helps the system to extract and learn about the features present in a particular scenario using Machine Learning.
Keywords: Application, Augmented Reality, AR Core SDK, Indoor Positioning and Navigation.
Scope of the Article: Machine Learning