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Erschienen in:
2015 | OriginalPaper | Buchkapitel
General Object Tip Detection and Pose Estimation for Robot Manipulation
verfasst von : Dadhichi Shukla, Özgür Erkent, Justus Piater
Erschienen in: Computer Vision Systems
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Abstract
Robot manipulation tasks like inserting screws and pegs into a hole or automatic screwing require precise tip pose estimation. We propose a novel method to detect and estimate the tip of elongated objects. We demonstrate that our method can estimate tip pose to millimeter-level accuracy. We adopt a probabilistic, appearance-based object detection framework to detect pegs and bits for electric screw drivers. Screws are difficult to detect with feature- or appearance-based methods due to their reflective characteristics. To overcome this we propose a novel adaptation of RANSAC with a parallel-line model. Subsequently, we employ image moments to detect the tip and its pose. We show that the proposed method allows a robot to perform object insertion with only two pairs of orthogonal views, without visual servoing.