Abstract
This paper proposes a pose estimation method for random bin picking with random forest. Main contribution of the method consists of two parts. First, we provide novel 3D feature called cube based feature (CF) and pairs with segment based feature (PSF). To compute the proposed feature, it is not necessary to define the local coordinate system. Thus, the proposed 3D feature is strongly robust against measurement noise, which causes difficulty computing the local coordinate system with high repeatability. Second, we propose to utilize the random forest for correspondences finding between point cloud of scene and a model. Experimental results show that the proposed method can achieve high accuracy pose estimation of industrial parts.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec)
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
