A\(^3\)SAR: Context-Aware Spatial Augmented Reality for Anywhere, Anyone, and Analysis

  • Benjin Mei
  • Dehai Liu
  • Xike Xie
  • Jinchuan Chen
  • Xiaoyong Du
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9052)


Internet and camera equipped mobile devices with versatile capabilities and inexpensive costs make it possible for a Spatial Augmented Reality (SAR) platform. In general, the SAR is to enhance the sensing of the real world by combining overlay data on top of the view. It supports applications in medical management, urban projects, and online gaming etc. Nevertheless, existing systems are mostly focus on visualizing formatted information on mobile devices. They are short in exploiting the profound nature of the reality. In this paper, we propose a novel context-aware platform, called A\(^3\)SAR, which augments the realities under three contexts: Anywhere Augmentation, Anyone Augmentation, and Analysis Augmentation. A\(^3\)SAR aims at seamlessly integrating the virtual and real worlds by incorporating emerging technologies from different dimensions. For the Anywhere Augmentation, the overlay is constructed based on the semantics extracted from websites with geospatial information (e.g., upcoming shuffles at a bus station, or the history of an antique in museum). For the Anybody Augmentation, the overlay is built according to users preferences and profiles (e.g., for a piano, visualizing music for a player, but visualizing maintenance instructions for a tuner). More than just loading pre-existing information, the Analysis Augmentation also provides analytical data dynamically (e.g., visualizing the most endangered spots in a fire accident). However, challenges rise in several aspects: (1) efficiency for handling concurrent service requests, especially analytical tasks; (2) overlay accuracy regarding noisy information; (3) semantic extraction from heterogeneous sources. We propose a series of technical solutions: we design an intelligent engine for efficient analytical overlays; we improve the calibration accuracy by addressing spatial imprecision; we tackle the heterogeneous modeling problem by considering a semantic web based solution. The real and the virtual, two worlds in parallel, have intersected at SAR, and are converged within A\(^3\)SAR.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Benjin Mei
    • 1
  • Dehai Liu
    • 1
  • Xike Xie
    • 2
  • Jinchuan Chen
    • 3
  • Xiaoyong Du
    • 1
  1. 1.School of InformationRenmin University of ChinaBeijingChina
  2. 2.Department of Computer ScienceAalborg UniversityAalborgDenmark
  3. 3.Key Laboratory of Data Engineering and Knowledge EngineeringRenmin University of China, MOEBeijingChina

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