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We present a novel learning algorithm especially designed for challenging, large-scale classification problems in mobile robotics. Our method addresses two important aims: first it reduces the required amount of interaction with a human supervisor, which increases the level of autonomy of the learning process. And second, it has the capability to update its internal representation online with every...
The goal of our work is to provide a fast and accurate method to estimate the camera motion from RGB-D images. Our approach registers two consecutive RGB-D frames directly upon each other by minimizing the photometric error. We estimate the camera motion using non-linear minimization in combination with a coarse-to-fine scheme. To allow for noise and outliers in the image data, we propose to use a...
In this paper, we consider the problem of tracking human motion with a 22-DOF kinematic model from depth images. In contrast to existing approaches, our system naturally scales to multiple sensors. The motivation behind our approach, termed Multiple Depth Camera Approach (MDCA), is that by using several cameras, we can significantly improve the tracking quality and reduce ambiguities as for example...
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