Augmented Reality and GPS-Based Resource Efficient Navigation System for Outdoor Environments: Integrating Device Camera, Sensors, and Storage

Saravjeet Singh, Jaiteg Singh (), Babar Shah, Sukhjit Sehra and Farman Ali ()
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Saravjeet Singh: Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab 140401, India
Jaiteg Singh: Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab 140401, India
Babar Shah: College of Technological Innovation, Zayed University, Dubai 19282, United Arab Emirates
Farman Ali: Department of Software, Sejong University, Seoul 05006, Korea

Sustainability, 2022, vol. 14, issue 19, 1-17

Abstract: Contemporary navigation systems rely upon localisation accuracy and humongous spatial data for navigational assistance. Such spatial-data sources may have access restrictions or quality issues and require massive storage space. Affordable high-performance mobile consumer hardware and smart software have resulted in the popularity of AR and VR technologies. These technologies can help to develop sustainable devices for navigation. This paper introduces a robust, memory-efficient, augmented-reality-based navigation system for outdoor environments using crowdsourced spatial data, a device camera, and mapping algorithms. The proposed system unifies the basic map information, points of interest, and individual GPS trajectories of moving entities to generate and render the mapping information. This system can perform map localisation, pathfinding, and visualisation using a low-power mobile device. A case study was undertaken to evaluate the proposed system. It was observed that the proposed system resulted in a 29 percent decrease in CPU load and a 35 percent drop in memory requirements. As spatial information was stored as comma-separated values, it required almost negligible storage space compared to traditional spatial databases. The proposed navigation system attained a maximum accuracy of 99 percent with a root mean square error value of 0.113 and a minimum accuracy of 96 percent with a corresponding root mean square value of 0.17.

Keywords: location awareness; prospective memory; embedded navigational intelligence; vision services; sustainable urban innovation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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