A Robust Pixel-Wise Prediction Network With Applications to Industrial Robotic Grasping

Abstract

Accurate object detection and 6D pose estimation are the key technologies in robotic grasping applications, where efficiency and robustness are the two most desirable goals. Especially for textureless industrial parts, it is difficult for most existing methods to extract robust image features from cluttered scenarios with heavy occlusion. To address this challenge, we propose a novel pixel-wise prediction strategy using local features to infer global information based on the inherent local-global relations of rigid objects. This strategy is robust to missing or disturbed local information since each pixel has an independent prediction, and the dense prediction manner can mitigate the instability caused by outliers. Accordingly, we first generate dense pixel-wise predictions of the object category, center, and keypoint from image features extracted by an encoder-decoder network. Then, these predictions are used to vote on and identify the keypoint locations of the specific instance object, and finally, the poses are estimated from the keypoints by an uncertainty <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PnP</i> (Perspective-n-Point) algorithm. Experiments on various scenarios are implemented to illustrate the advantages of our approach on severe industrial scenes, and a robotic grasping platform is constructed to evaluate its application performance.