Abstract
Abstract. Augmented Reality (AR) is already transforming many fields, from medical applications to industry, entertainment and heritage. In its most common form, AR expands reality with virtual 3D elements, providing users with an enhanced and enriched experience of the surroundings. Until now, most of the research work focused on techniques based on markers or on GNSS/INS positioning. These approaches require either the preparation of the scene or a strong satellite signal to work properly. In this paper, we investigate the use of visual-based methods, i.e., methods that exploit distinctive features of the scene estimated with Visual Simultaneous Localization and Mapping (V-SLAM) algorithms, to determine and track the user position and attitude. The detected features, which encode the visual appearance of the scene, can be saved and later used to track the user in successive AR sessions. Existing AR frameworks like Google ARCore, Apple ARKit and Unity AR Foundation recently introduced visual-based localization in their frameworks, but they target mainly small scenarios. We propose a new Mobile Augmented Reality (MAR) methodology that exploits OPEN-V-SLAM to extend the application range of Unity AR Foundation and better handle large-scale environments. The proposed methodology is successfully tested in both controlled and real-case large heritage scenarios. Results are available also in this video: https://youtu.be/Q7VybmiWIuI.
Highlights
Augmented Reality (AR) aims to blend the real and the digital worlds, enabling immersive and interactive experiences where the reality is fused with virtual content perceivable by humans as visual, auditory or haptic stimuli (Bekele and Champion, 2019)
By combining OPEN-Visual Simultaneous Localization and Mapping (V-SLAM) (Sumikura et al, 2019) with Unity AR Foundation, we present how to enable accurate and markerless Mobile Augmented Reality (MAR) experiences in large-scale outdoor scenarios
PTAM (Klein and Murray, 2007) represented an important paradigm change, proposing to split localization and mapping in two different concurrent operations, and replacing probabilistic state estimation with a least square optimization. They proposed to update the state of the map only on selected frames, called keyframes, reducing the overall system weight and allowing the V-SLAM algorithm to keep a real-time behaviour in bigger scenarios
Summary
Augmented Reality (AR) aims to blend the real and the digital worlds, enabling immersive and interactive experiences where the reality is fused with virtual content perceivable by humans as visual, auditory or haptic stimuli (Bekele and Champion, 2019). Marker-based tracking works by identifying, in the acquired images, known physical targets or objects previously placed in the scene This approach has been widely applied, thanks to its robustness and low computational requirements. Nakamura et al, 2018) or local coordinate systems In the latter case, the tracking can exploit feature maps of the environment, known as sparse point clouds, typically estimated with Visual Simultaneous Localization and Mapping (V-SLAM) algorithms (Cadena et al, 2016; Sualeh and Kim, 2019). AR sessions can persist after their termination, allowing different users to resume them successively, and visualize the original augmented content Without requiring markers, these solutions take advantage of the 3D visual features of the scene estimated with the build-in V-SLAM algorithms during the creation of the AR session. 3D reconstructions of the ancient pile-dwelling structures are superimposed to the wooden remains, partially emerging from the lake water surface, to show how such houses were formed and located on the lake
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