Deep Workpiece Region Segmentation for Bin Picking

Autor:	Khalid, Muhammad Usman, Hager, Janik M., Kraus, Werner, Huber, Marco F., Toussaint, Marc
Rok vydání:	2019
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning Computer Science - Robotics
Druh dokumentu:	Working Paper
Popis:	For most industrial bin picking solutions, the pose of a workpiece is localized by matching a CAD model to point cloud obtained from 3D sensor. Distinguishing flat workpieces from bottom of the bin in point cloud imposes challenges in the localization of workpieces that lead to wrong or phantom detections. In this paper, we propose a framework that solves this problem by automatically segmenting workpiece regions from non-workpiece regions in a point cloud data. It is done in real time by applying a fully convolutional neural network trained on both simulated and real data. The real data has been labelled by our novel technique which automatically generates ground truth labels for real point clouds. Along with real time workpiece segmentation, our framework also helps in improving the number of detected workpieces and estimating the correct object poses. Moreover, it decreases the computation time by approximately 1s due to a reduction of the search space for the object pose estimation. Comment: IEEE CASE 2019
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1909.03462 Zobrazit plný text záznamu View this record from Arxiv