Abstract

Mobile map applications such as Google Maps often don’t provide detailed information about facility areas such as amusement parks and university campuses. In addition, there are some people who cannot reach their destination just by reading a flat map. Therefore, in this research, we have developed an AR (Augmented Reality) navigation application for facilities to solve these problems. In addition, by using Kalman filtering to estimate user position, we could improve the accuracy of AR objects display.

Highlights

  • People increasingly use smartphone navigation apps instead of paper maps when traveling

  • Navigation of outdoor sites using such map apps has the following problems: 1. Detailed information needed to get to destination is often not included

  • There exist navigation apps specialized for particular facilities. Examples of these include the Tokyo Disney Resort app [6] and Tokyo Parks Navi [7]. Unlike map apps such as Google Maps, these apps specialize in information regarding particular premises, so it is possible to navigate even in nonpublic areas where navigation is not supported with general map apps

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Summary

Introduction

People increasingly use smartphone navigation apps instead of paper maps when traveling. A user’s destination is not the site itself but a facility located within the site These map apps often do not provide information about on-site facilities. According to [2], the number of people who get lost can decrease dramatically by preparing a guide using photographs and sentences based on visual information from landmarks for those who cannot read maps well. Such users are more likely to be able to reach their. This system is equipped with a Kalman filter that integrates data from inertial sensors in addition to the GNSS position sensing

Navigation apps which are published currently
Methods of Navigation
Map services
Development environment
Facility and destination selection
AR navigation to the destination
Matching of AR space and real space
AR system
Updating coordinates of AR camera object
System configuration
User position estimation using Kalman filtering
Time update formula
Experiment and result
Findings
Conclusion
Full Text
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