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This paper investigates possible physical alterations of tracking targets to obtain improved 6DoF pose detection for a camera observing the known targets. We explore the influence of several texture characteristics on the pose detection, by simulating a large number of different target objects and camera poses. Based on statistical observations, we rank the importance of characteristics such as texturedness...
Outdoor Augmented Reality typically requires tracking in unprepared environments. For global registration, Global Positioning System (GPS) is currently the best sensing technology, but its precision and update rate are not sufficient for high quality tracking. We present a system that uses Kalman filtering for fusion of Differential GPS (DGPS) or Real-Time Kinematic (RTK) based GPS with barometric...
In this work we present a novel idea of evaluating natural feature-point based tracking targets. Our main objective is to evaluate the inherent characteristics of natural feature-point sets with respect to vision-based pose estimation algorithms. Our work attempts to break new ground by concentrating on evaluating complete tracking targets, rather than evaluating tracking methods or single features...
In this paper we present a novel method for real-time pose estimation and tracking on low-end devices such as mobile phones. The presented system can track multiple known targets in real-time and simultaneously detect new targets for tracking. We present a method to automatically and dynamically balance the quality of detection and tracking to adapt to a variable time budget and ensure a constant...
We present a fast and memory efficient method for localizing a mobile user's 6DOF pose from a single camera image. Our approach registers a view with respect to a sparse 3D point reconstruction. The 3D point dataset is partitioned into pieces based on visibility constraints and occlusion culling, making it scalable and efficient to handle. Starting with a coarse guess, our system only considers features...
In this paper we present two techniques for natural feature tracking in real-time on mobile phones. We achieve interactive frame rates of up to 20 Hz for natural feature tracking from textured planar targets on current-generation phones. We use an approach based on heavily modified state-of-the-art feature descriptors, namely SIFT and Ferns. While SIFT is known to be a strong, but computationally...
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