Taragay Oskiper

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Over the past decade, tremendous amount of research activity has focused around the problem of localization in GPS denied environments. Challenges with localization are highlighted in human wearable systems where the operator can freely move through both indoors and outdoors. In this paper, we present a robust method that addresses these challenges using a(More)
Our goal is to create a visual odometry system for robots and wearable systems such that localization accuracies of centimeters can be obtained for hundreds of meters of distance traveled. Existing systems have achieved approximately a 1% to 5% localization error rate whereas our proposed system achieves close to 0.1% error rate, a ten-fold reduction.(More)
In this paper we present an augmented reality binocular system to allow long range high precision augmentation of live telescopic imagery with aerial and terrain based synthetic objects, vehicles, people and effects. The inserted objects must appear stable in the display and must not jitter and drift as the user pans around and examines the scene with the(More)
In this paper, we present a unified approach for a camera tracking system based on an error-state Kalman filter algorithm. The filter uses relative (local) measurements obtained from image based motion estimation through visual odometry, as well as global measurements produced by landmark matching through a pre-built visual landmark database and range(More)
In this paper, we study how to build a vision-based system for global localization with accuracies within 10 cm. for robots and humans operating both indoors and outdoors over wide areas covering many square kilometers. In particular, we study the parameters of building a landmark database rapidly and utilizing that database online for real-time accurate(More)
In this paper, we present a unified approach for a drift-free and jitter-reduced vision-aided navigation system. This approach is based on an error-state Kalman filter algorithm using both relative (local) measurements obtained from image based motion estimation through visual odometry, and global measurements as a result of landmark matching through a(More)
We present a system that combines multiple visual navigation techniques to achieve GPS-denied, non-line-of-sight SLAM capability for heterogeneous platforms. Our approach builds on several layers of vision algorithms, including sparse frame-to-frame structure from motion (visual odometry), a Kalman filter for fusion with inertial measurement unit (IMU) data(More)
A pilot study was conducted to ascertain the level of agreement between auscultatory findings derived from heart sound recordings by a cardiologist and the results of a computer-based heart sound analysis algorithm. Heart sound recordings were obtained from volunteer subjects previously diagnosed with hypertrophic cardiomyopathy. Twenty-second recordings(More)