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We propose a probabilistic filtering method which fuses joint measurements with depth images to yield a precise, real-time estimate of the end-effector pose in the camera frame. This avoids the need for frame transformations when using it in combination with visual object tracking methods. Precision is achieved by modeling and correcting biases in the joint measurements as well as inaccuracies in...
Many calibration methods calibrate a pair of sensors at a time. For robotic systems with many sensors, they are often time-consuming to use, and can also lead to inaccurate results. In this paper, we combine a number of ideas in the literature to derive a unified framework that jointly calibrates many sensors a large system. Key to our approach are (i) grouping sensors to produce 3D data, thereby...
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