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We propose a novel multiple model fitting method based on outlier insensitive evolutionary dynamics, fulfilling several important requirements. Our method automatically identifies a unspecified number of models and is robust to noise and outliers in the data. Furthermore, we are able to handle overlapping models, by allowing that data points are assigned to more than one model. This is implicitly...
In this paper, we discuss how the sensors available in modern smartphones can improve 6-degree-of-freedom (6DOF) localization in wide-area environments. In our research, we focus on phones as a platform for large-scale Augmented Reality (AR) applications. Thus, our aim is to estimate the position and orientation of the device accurately and fast — it is unrealistic to assume that users are willing...
Localization of mobile devices is an essential task in Augmented Reality and has therefore been an active research topic for many years. Typically, indoor tracking approaches, such as methods based on infrared or ultra-wide-band, require preparations of the environment and special hardware sensors. Conversely, image feature tracking approaches can provide orientation estimates without special tracking...
We present a novel method for the real-time creation and tracking of panoramic maps on mobile phones. The maps generated with this technique are visually appealing, very accurate and allow drift-free rotation tracking. This method runs on mobile phones at 30 Hz and has applications in the creation of panoramic images for offline browsing, for visual enhancements through environment mapping and for...
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