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Knowledge of 3-D object shape is of great importance to robot manipulation tasks, but may not be readily available in unstructured environments. While vision is often occluded during robot-object interaction, high-resolution tactile sensors can give a dense local perspective of the object. However, tactile sensors have limited sensing area and the shape representation must faithfully approximate non-contact areas. In addition, a key challenge is efficiently incorporating these dense tactile measurements into a 3-D mapping framework. In this work, we propose an incremental shape mapping method using a GelSight tactile sensor and a depth camera. Local shape is recovered from tactile images via a learned model trained in simulation. Through efficient inference on a spatial factor graph informed by a Gaussian process, we build an implicit surface representation of the object. We demonstrate visuo-tactile mapping in both simulated and real-world experiments, to incrementally build 3-D reconstructions of household objects.
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Suresh, Sudharshan ; Bauza, Maria ; Yu, Kuan-Ting ; Mangelson, Joshua G. ; Rodriguez, Alberto ; Kaess, Michael ( , IEEE International Conference on Robotics and Automation (ICRA))
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Hsiao, Ming ; Mangelson, Joshua G. ; Suresh, Sudharshan ; Debrunner, Christian ; Kaess, Michael ( , IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))