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Conference Paper
Bin Picking Method Using Multiple Local Features
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- Authors
- Kyekyung Kim, Sangseung Kang, Jaehong Kim, Jaeyeon Lee, Joonbae Kim
- Issue Date
- 2015-10
- Citation
- International Conference on Ubiquitous Robots and Ambient Intelligence (URAI) 2015, pp.148-150
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- Bin-picking technology using vision sensor for picking objects has studied intensively because it results in productivity improvement by applying automation process in industry fields. To obtain more accurate result of position detection and pose estimation of objects to be picked using robot system is not trivial task because of poor factors such as nonuniform lighting condition, occlusion, pose variation. In this paper, vision based object detection and pose estimation method for bin-picking are proposed that provides high accuracy for detecting object position and estimating distance be offered to industrial robot. Multiple local features are extracted and recognized for detecting object position and estimating pose of a picking object among randomly piled objects in a supply bin. We have simulated to evaluate performance of position detection and pose estimation of object using database captured under various lighting condition and in a pilot system, which has built alike a production site.
- KSP Keywords
- Bin-picking, High accuracy, Industrial robot, Lighting condition, Local features, Pose estimation, Productivity improvement, Vision sensor, automation process, estimation method, object detection