General Object Tip Detection and Pose Estimation for Robot Manipulation

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 featureor appearancebased 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.