Abstract In augmented reality applications it is essential to know the position and orientation of the user to correctly register virtual 3D content in the user’s field of view. For this purpose, visual tracking through simultaneous localization and mapping (SLAM) is often used. However, when applied to the commonly occurring situation where the users are mostly stationary, many methods presented in previous research have two key limitations. First, SLAM techniques alone do not address the problem of global localization with respect to prior models of the environment. Global localization is essential in many applications where multiple users are expected to track within a shared space, such as spectators at a sporting event. Secondly, these methods often assume significant translational movement to accurately reconstruct and track from a local model of the environment, causing challenges for many stationary applications. In this paper, we extend recent research on Spherical Localization and Tracking to support relocalization after tracking failure, as well as global localization in large shared environments, and optimize the method for operation on mobile hardware. We also evaluate various state-of-the-art localization approaches, the robustness of our visual tracking method, and demonstrate the effectiveness of our system in real-life scenarios.

Hand-held capture of stereo panoramas involves spinning the camera in a roughly circular path to acquire a dense set of views of the scene. However, most existing structure-from-motion pipelines fail when trying to reconstruct such trajectories, due to the small baseline between frames. In this work, we evaluate the use of spherical structure-from-motion for reconstructing handheld stereo panorama captures. The spherical motion constraint introduces a strong regularization on the structure-from-motion process which mitigates the small-baseline problem, making it well-suited to the use case of stereo panorama capture with a handheld camera. We demonstrate the effectiveness of spherical structure-from-motion for casual capture of high-resolution stereo panoramas and validate our results with a user study.

When implementing an Augmented Reality (AR) interface, it is essential to track camera motion in order to precisely register the virtual overlay in the view of the user. However, unlike most indoor AR scenarios, in many outdoor scenarios the user maintains a static position performing mostly rotational movements. Simultaneous Localization and Mapping (SLAM) methods typically used to solve the tracking problem require significant translational camera motion to perform reliably. The magnitude of the required translation is proportional to the size of the scene, exacerbating this problem in large environments such as open places or stadiums. In this paper, we present an alternative SLAM method, which combines spherical Structure-from-Motion and a robust 3D tracking method. We compare our method to ORB SLAM2 in synthetic and real tests, and show that our method can track more reliably in large spaces, with simpler calculation due to the spherical motion constraint. We discuss this issue in the context of implementing an AR interface for live sport events in stadiums or other open environments, but possible application scenarios for our technique go beyond and can be applied to handheld AR in many outdoor environments.

Augmented Reality (AR) sandtables facilitate the shaping of sand to form a surface that is transformed into a digital terrain map which is projected back onto the sand. Although a mature technology, there are still few instances of sandtables being used in surface analysis. Fundamentally there has not been any reported formal assessment of how well sandtables perform in an educational context compared to other conventional learning environments. We compared learning outcomes from using an AR sandtable versus a conventional 3D GIS to convey key concepts in terrain and hydrological analyses via usability and knowledge testing. Overall results from students at a research-intensive New Zealand university reveal a faster task performance and more learning satisfaction when using the sandtable to undertake experimental tasks. Effectiveness and knowledge quiz results revealed no significant difference between the technologies though there was a trend for more accurate answers with 3D GIS tasks. Student learning wise, the sandtable integrated core concepts (especially morphometry) more effectively though both technologies were otherwise similar. We conclude that sandtables have high potential in geospatial teaching, fostering accessible and engaging means of introducing terrain and hydrological concepts, prior to undertaking a more accurate and precise surface analysis with 3D GIS.

Hand-held capture of stereo panoramas involves spinning the camera in a roughly circular path to acquire a dense set of views of the scene. However, most existing structure-from-motion pipelines fail when trying to reconstruct such trajectories, due to the small baseline between frames. In this work, we propose to use spherical structure-from-motion for reconstructing handheld stereo panorama captures. Our initial results show that spherical motion constraints are critical for reconstructing small-baseline, circular trajectories.