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Estimation and Inference for Grasping and Manipulation Tasks Using Vision and Kinesthetic Sensors
[thesis]
2013
This thesis presents a novel framework for state estimation in the context of robotic grasping and manipulation. The overall estimation approach is based on fusing various visual cues for manipulator tracking, namely appearance and feature-based, shape-based, and silhouette-based visual cues. Similarly, a framework is developed to fuse the above visual cues, but also kinesthetic cues such as force-torque and tactile measurements, for in-hand object pose estimation. The cues are extracted from
doi:10.7907/pzb6-qj39
fatcat:fknqpkkezndf3pm6fk5jxr6wxa