Abstract
Pose estimation is a particularly important link in the task of robotic bin-picking. Its purpose is to obtain the 6D pose (3D position and 3D posture) of the target object. In real bin-picking scenarios, noise, overlap, and occlusion affect accuracy of pose estimation and lead to failure in robot grasping. In this paper, a new point-pair feature (PPF) descriptor is proposed, in which curvature information of point-pairs is introduced to strengthen feature description, and improves the point cloud matching rate. The proposed method also introduces an effective point cloud preprocessing, which extracts candidate targets in complex scenarios, and, thus, improves the overall computational efficiency. By combining with the curvature distribution, a weighted voting scheme is presented to further improve the accuracy of pose estimation. The experimental results performed on public data set and real scenarios show that the accuracy of the proposed method is much higher than that of the existing PPF method, and it is more efficient than the PPF method. The proposed method can be used for robotic bin-picking in real industrial scenarios.
Highlights
Bin-picking is a common scene in the industry, aiming to take out objects placed in disorder by robotic arms
Only point cloud of unobstructed target objects in the scene are retained; (2) A new point-pair feature descriptor is proposed, which introduces curvature information based on the PPF method to effectively enhance the description of point-pair features; (3) In the pose voting link, a new weighted voting scheme is proposed by combining the curvature distribution of the model, which gives more weight to high information point-pairs, thereby further improving the accuracy of pose estimation
We propose a 6D pose estimation method based on a new point-pair feature descriptor
Summary
Bin-picking is a common scene in the industry, aiming to take out objects placed in disorder by robotic arms. Local voting usually refers to the vote of each pixel or 3D point to obtain the final 6D poses of target objects Such methods are often used in scenarios where there are no texture and overlapping occlusions among objects, which are suitable for robotic arms to perform bin-picking tasks. Only point cloud of unobstructed target objects in the scene are retained; (2) A new point-pair feature descriptor is proposed, which introduces curvature information based on the PPF method to effectively enhance the description of point-pair features; (3) In the pose voting link, a new weighted voting scheme is proposed by combining the curvature distribution of the model, which gives more weight to high information point-pairs, thereby further improving the accuracy of pose estimation.
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