Andrew Calway

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Most work in visual augmented reality (AR) employs predefined markers or models that simplify the algorithms needed for sensor positioning and augmentation but at the cost of imposing restrictions on the areas of operation and on interactivity. This paper presents a simple game in which an AR agent has to navigate using real planar surfaces on objects that(More)
In this paper, we describe a novel method for discovering and incorporating higher level map structure in a real-time visual simultaneous localization and mapping (SLAM) system. Previous approaches use sparse maps populated by isolated features such as 3-D points or edgelets. Although this facilitates efficient localization, it yields very limited scene(More)
We present a method for the learning and detection of multiple rigid texture-less 3D objects intended to operate at frame rate speeds for video input. The method is geared for fast and scalable learning and detection by combining tractable extraction of edgelet constellations with library lookup based on rotationand scale-invariant descriptors. Most(More)
We describe a robust system for vision-based SLAM using a single camera which runs in real-time, typically around 30 fps. The key contribution is a novel utilisation of multi-resolution descriptors in a coherent top-down framework. The resulting system provides superior performance over previous methods in terms of robustness to erratic motion, camera(More)
Two major limitations of real-time visual SLAM algorithms are the restricted range of views over which they can operate and their lack of robustness when faced with erratic camera motion or severe visual occlusion. In this paper we describe a visual SLAM algorithm which addresses both of these problems. The key component is a novel feature description(More)
We describe a particle filtering method for vision based tracking of a hand held calibrated camera in real-time. The ability of the particle filter to deal with non-linearities and non-Gaussian statistics suggests the potential to provide improved robustness over existing approaches, such as those based on the Kalman filter. In our approach, the particle(More)
Previous work on visual SLAM has shown that indexing on space and scale facilitates the use of feature descriptors for matching in real-time systems and that this can significantly increase robustness. However, the performance gains necessarily diminish as uncertainty about camera position increases. In this paper we address this issue by introducing a(More)