## 5 Conclusions  
The aim of this study is to effectively solve problems such as detection failure or difficulty in locating the grasping points for irregular sheet metal parts when random stacking occurs. In this research, a part identification and pose estimation approach is proposed. The part information is obtained by establishing a two-dimensional code decoding framework for parts. To enhance the reliability of part recognition, a contour  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Positioning Errors** & **Mean Errors** & **Root Mean Square Errors** \\ \hline x direction/mm & 0.65 & 0.74 \\ y direction/mm & \(-\)0.21 & 0.58 \\ position angle/(\({}^{\circ}\)) & 0.47 & 0.54 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mean and root mean square errors in the positioning of occluded parts.  
Figure 21: Results of stacked part positioning in different situations: (**a**) approximately 20% of the part is occluded, (**b**) approximately 40% of the part is occluded, (**c**) the part is occluded by parts that are not the same shape, and (**d**) multiple parts are stacked together.  
template matching model is built for occluded parts. Location estimation results are obtained by combining a priori contour information with geometric features. A stereo vision system is constructed to perform recognition and localization experiments on the occluded parts. The experimental results show that the algorithm can accurately locate and extract two-dimensional laser-generated code features and improve the decoding rate; the recognition algorithm has an accuracy of higher than 90% at occlusion rates of less than 40%, and the average recognition rate can reach 93%; the localization algorithm can effectively capture geometric features; the localization error is less than 0.8 mm; and the pose angle error is less than 0.6\({}^{\circ}\). The accuracy and stability of the method are both high.  
In this paper, the accuracy of data collected by a 1.3-megapixel industrial camera is low, and the accuracy will be higher using a higher-resolution data collection device. One type of long irregular part is tested in this paper; if more irregular parts have been experimented with, the stability of the algorithm will be better verified. The algorithm in this paper is mainly for thin parts, and the algorithm for parts' detection with certain thicknesses needs to be improved in the future.  
Conceptualization, R.L. and J.F.; methodology, J.F.; validation, J.F., F.Z., and Z.H.; data curation, Z.H.; writing--original draft preparation, J.F.; writing--review and editing, R.L., F.Z., and Z.H.; visualization, J.F. and Z.H.; supervision, R.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Science and Technology Foundation of State Key Laboratory grant number 2022-JCJQ-L8-015-0201, Liaoning Provincial Department of Education Scientific Research Funding Project grant number LJKZ0475, and Dalian High-Level Talent Innovation Support Program grant number 2022 RJ03.  
Not applicable.  
Data Availability StatementData that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.