Work

Data-driven grasping with partial sensor data

Year: 2009

Type: conference-paper

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.

Source: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

DOI: 10.1109/iros.2009.5354078

Authors: Corey Goldfeder, Matei Ciocarlie, Jaime Peretzman, Hao Dang, Peter K. Allen

Institution: Columbia University

Language: English

Topic: Robot Manipulation and Learning

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2157692698

FWCI: 14.5

Cites: 36

Cited by: 84

Related to: 10