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A novel method for finding grasping handles in a clutter using RGBD Gaussian mixture models

Published online by Cambridge University Press:  16 June 2021

Olyvia Kundu ,
Samrat Dutta  and
Swagat Kumar Open the ORCID record for Swagat Kumar [Opens in a new window]
Olyvia Kundu
Affiliation:
TATA Consultancy Services, Bangalore560066, India
Samrat Dutta
Affiliation:
TATA Consultancy Services, Bangalore560066, India
Swagat Kumar*
Affiliation:
TATA Consultancy Services, Bangalore560066, India
*
*Corresponding author. Email: swagat.kumar@tcs.com
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Abstract

The paper proposes a novel method to detect graspable handles for picking objects from a confined and cluttered space, such as the bins of a rack in a retail warehouse. The proposed method combines color and depth curvature information to create a Gaussian mixture model that can segment the target object from its background and imposes the geometrical constraints of a two-finger gripper to localize the graspable regions. This helps in overcoming the limitations of a poorly trained deep network object detector and provides a simple and efficient method for grasp pose detection that does not require a priori knowledge about object geometry and can be implemented online with near real-time performance. The efficacy of the proposed approach is demonstrated through simulation as well as real-world experiment.

Keywords

grasp pose detectiongraspable affordancegraspingRGBD point cloudGaussian mixture model (GMM)surface normalsregion growing algorithmsprimitive shape identification
Type
Article
Information
Robotica , Volume 40 , Issue 3 , March 2022 , pp. 447 - 463
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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