Title :
Fusing visual and tactile sensing for 3-D object reconstruction while grasping
Author :
Ilonen, Jarmo ; Bohg, Jeannette ; Kyrki, Ville
Author_Institution :
Machine Vision & Pattern Recognition Res. Group, Lappeenranta Univ. of Technol., Lappeenranta, Finland
Abstract :
In this work, we propose to reconstruct a complete 3-D model of an unknown object by fusion of visual and tactile information while the object is grasped. Assuming the object is symmetric, a first hypothesis of its complete 3-D shape is generated from a single view. This initial model is used to plan a grasp on the object which is then executed with a robotic manipulator equipped with tactile sensors. Given the detected contacts between the fingers and the object, the full object model including the symmetry parameters can be refined. This refined model will then allow the planning of more complex manipulation tasks. The main contribution of this work is an optimal estimation approach for the fusion of visual and tactile data applying the constraint of object symmetry. The fusion is formulated as a state estimation problem and solved with an iterative extended Kalman filter. The approach is validated experimentally using both artificial and real data from two different robotic platforms.
Keywords :
Kalman filters; image reconstruction; manipulators; nonlinear filters; path planning; sensor fusion; solid modelling; state estimation; tactile sensors; 3-D model; 3-D object reconstruction; 3-D shape; data fusion; iterative extended Kalman filter; object symmetry constraint; optimal estimation; planning; robotic manipulator; state estimation problem; symmetry parameters; tactile sensing; tactile sensors; visual sensing; Grasping; Robot kinematics; Tactile sensors; Uncertainty; Visualization;
Conference_Titel :
Robotics and Automation (ICRA), 2013 IEEE International Conference on
Conference_Location :
Karlsruhe
Print_ISBN :
978-1-4673-5641-1
DOI :
10.1109/ICRA.2013.6631074