Abstract: Aiming at the problems of different sizes,occlusion stacking and lighting changes of weakly textured workpieces in industrial scenes,a monocular 6D pose estimation method RAAS-PVNet is proposed.The design resolution adaptive rectangular convolutional RARConv dynamically adjusts the size of the convolutional kernel and the number of sampling points,which solves the problem of the insufficient ability of the traditional convolutional structure in modeling multi-scale information.The angular distance collaborative weighted voting strategy AS is proposed,the vertical distance constraint of the direction vector extension line is introduced,and the credibility of each voting point is accurately measured by combining the continuous weight fusionmecha-nism,so that the voting results are focused on high-quality points and the anti-occlusion ability of the model is improved.Faced with the problem of lack of industrial part datasets in the field of pose estimation,a dataset production method combining real data and synthetic data in proportion is designed to construct the workpiece dataset 6DInd.Experiments show that the 2D Projection and ADD(-S) of RAAS-PVNet on 6DInd are increased by 10.22% and 10.26%,respectively,and have good robustness under occlusion and lighting changes,and the processing speed of 30 fps meets the real-time requirements.

Key words: 6D pose estimation, RAAS-PVNet, Resolution adaptive rectangular convolution, Angular spacing collaborative weighted voting strategy, 6DInd