Detection of parametrized 3-D primitives from stereo for robotic grasping

Laura Fernandez Gallardo*, Ville Kyrki

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

5 Citations (Scopus)


The grasping skill is an indispensable quality for general service robotics. In a home-like natural environment, manipulated objects may be unknown in advance, which prevents the use of a combination of traditional grasp planning and visual pose estimation to realize grasping. Stereo vision is an inexpensive and relatively general sensor for 3-D objects. However, the quality of the sensor data from a stereo camera can be restrictingly low for grasping. This paper proposes an approach to extract parametrized 3-D primitives, which describe an object's overall shape, as well as its location, orientation, and size. These pieces of information are sufficient to grasp the object. Only a stereo image pair is used to generate a partial three-dimensional point cloud, which is then approximated by simple primitives, such as a box or a cylinder. The approach combines initial estimation using RANSAC and further iterative optimization of the unknown parameters. Experiments with real world objects show that the approach can be used to grasp a range of objects using low quality point clouds from single stereo pairs.

Original languageEnglish
Title of host publicationIEEE 15th International Conference on Advanced Robotics: New Boundaries for Robotics, ICAR 2011
Number of pages6
Publication statusPublished - 2011
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Advanced Robotics - Tallinn, Estonia
Duration: 20 Jun 201123 Jun 2011
Conference number: 15


ConferenceInternational Conference on Advanced Robotics
Abbreviated titleICAR


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