Goldfeder Corey author Columbia University. Computer Science Ciocarlie Matei author Columbia University. Computer Science Peretzman Jaime author Columbia University. Computer Science Dang Hao author Columbia University. Computer Science Allen Peter K. author Columbia University. Computer Science Columbia University. Computer Science originator text Articles 2009 English 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. Robotics Piscataway, N.J. IEEE 2009 1278 1283 http://dx.doi.org/10.1109/IROS.2009.5354078 http://hdl.handle.net/10022/AC:P:15088 NNC NNC 2012-10-25 15:52:07 -0400 2012-10-25 16:04:38 -0400 9086 eng