Title :
Data-driven grasping with partial sensor data
Author :
Goldfeder, Corey ; Ciocarlie, Matei ; Peretzman, Jaime ; Dang, Hao ; Allen, Peter K.
Author_Institution :
Dept. of Comput. Sci., Columbia Univ., New York, NY, USA
Abstract :
To grasp a novel object, we can index it into a database of known 3D models and use precomputed grasp data for those models to suggest a new grasp. We refer to this idea as data-driven grasping, and we have previously introduced the Columbia Grasp Database for this purpose. In this paper we demonstrate a data-driven grasp planner that requires only partial 3D data of an object in order to grasp it. To achieve this, we introduce a new shape descriptor for partial 3D range data, along with an alignment method that can rigidly register partial 3D models to models that are globally similar but not identical. Our method uses SIFT features of depth images, and encapsulates ¿nearby¿ views of an object in a compact shape descriptor.
Keywords :
data handling; dexterous manipulators; robot vision; Columbia Grasp Database; data-driven grasping; partial 3D models; partial sensor data; Geometry; Image databases; Intelligent robots; Intelligent sensors; Robot kinematics; Sensor systems and applications; Shape measurement; Solid modeling; Spatial databases; USA Councils;
Conference_Titel :
Intelligent Robots and Systems, 2009. IROS 2009. IEEE/RSJ International Conference on
Conference_Location :
St. Louis, MO
Print_ISBN :
978-1-4244-3803-7
Electronic_ISBN :
978-1-4244-3804-4
DOI :
10.1109/IROS.2009.5354078