Proposal of a Grasp Verification Method Utilizing Background Subtraction and Depth Information

Commonly grasp verification approach involves using the opening width of the robot's gripper.However, methods based on the opening width of the gripper may not apply to slender objects.In this study, we propose a grasp verification method using background subtraction.Our proposed method uses depth information to mask the background, isolating only the images of the gripper and the grasped object.Subsequently, a difference image is created by comparing the current image with the pre-grasp state, and the grasp state is detected based on the magnitude of the observed changes.The method minimizes environmental influences by masking the background, enabling highly accurate grasp verification even for complex objects.Through experiments, we validate the effectiveness of the proposed method.

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Publication Details

Journal
Proceedings of International Conference on Artificial Life and Robotics
Published
2025-02-13
DOI
https://doi.org/10.5954/icarob.2025.os17-6
Citations
1
Primary Topic
Software Testing and Debugging Techniques
Type
article
Field-Weighted Citation Impact
2.38

Funders

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article

Proposal of a Grasp Verification Method Utilizing Background Subtraction and Depth Information

Hakaru Tamukoh, Yuga Yano, Kimiyoshi Arimura, Ryo Terashima
1 citations
Proceedings of International Conference on Artificial Life and Robotics
Software Testing and Debugging Techniques
2.38
article

Proposal of a Grasp Verification Method Utilizing Background Subtraction and Depth Information

Hakaru Tamukoh, Yuga Yano, Kimiyoshi Arimura, Ryo Terashima
article en
1 citations

Abstract

Commonly grasp verification approach involves using the opening width of the robot's gripper.However, methods based on the opening width of the gripper may not apply to slender objects.In this study, we propose a grasp verification method using background subtraction.Our proposed method uses depth information to mask the background, isolating only the images of the gripper and the grasped object.Subsequently, a difference image is created by comparing the current image with the pre-grasp state, and the grasp state is detected based on the magnitude of the observed changes.The method minimizes environmental influences by masking the background, enabling highly accurate grasp verification even for complex objects.Through experiments, we validate the effectiveness of the proposed method.

Proceedings of International Conference on Artificial Life and RoboticsVol. 30
Kyushu Institute of Technology (JP)
Japan Society for the Promotion of Science, Advanced Low Carbon Technology Research and Development Program
Openalex Percentile: Top 7%
Software Testing and Debugging Techniques
2.38
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