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A key problem in mobile computing is providing people access to cyber-information associated with their surrounding physical objects. Mobile augmented reality is one of the emerging techniques that addresses this problem by allowing users to see the cyber-information associated with real-world physical objects by overlaying that cyber-information on the physical objects’ imagery. This paper presents a new vision-based context-aware approach for mobile augmented reality that allows users to query and access semantically-rich 3D cyber-information related to real-world physical objects and see it precisely overlaid on top of imagery of the associated physical objects. The approach does not require any RF-based location tracking modules, external hardware attachments on the mobile devices, and/or optical/fiducial markers for localizing a user’s position. Rather, the user’s 3D location and orientation are automatically and purely derived by comparing images from the user’s mobile device to a 3D point cloud model generated from a set of pre-collected photographs. Our approach supports content authoring where collaboration on editing the content stored in the 3D cloud is possible and content added by one user can be immediately accessible by others. In addition, a key challenge of scalability for mobile augmented reality is addressed in this paper. In general, mobile augmented reality is required to work regardless of users’ location and environment, in terms of physical scale, such as size of objects, and in terms of cyber-information scale, such as total number of cyber-information entities associated with physical objects. However, many existing approaches for mobile augmented reality have mainly tested their approaches on limited real-world use-cases and have challenges in scaling their approaches. By designing a multi-model based direct 2D-to-3D matching algorithms for localization, as well as applying a caching scheme, the proposed research consistently supports near real-time localization and information association regardless of users’ location, size of physical objects, and number of cyber-physical information items. Empirical results presented in the paper show that the approach can provide millimeter-level augmented reality across several hundred or thousand objects without the need for additional non-imagery sensor inputs.

Fast and scalable structure-from-motion based localization for high-precision mobile augmented reality systems