| Autor: |
FU Luhua, SUN Yujing, SUN Changku, WANG Peng, ZHANG Baoshang |
| Předmět: |
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| Zdroj: |
Journal of Measurement Science & Instrumentation; Sep2023, Vol. 14 Issue 3, p253-262, 10p |
| Abstrakt: |
The point cloud registration technique and 3D point cloud measurement technology without artificial marks are used to demonstrate a pose measurement solution in this study. The cross-source point cloud registration accuracy is relatively low due to the large density difference, especially when the measurement point cloud partially overlaps with the reference point cloud, and the typical point cloud registration procedure is susceptible to the initial position and noise. A deep learning-based end-to-end pose estimation algorithm is proposed in order to address the aforementioned issues. First, a point cloud hybrid feature coding module is created to combine the features of the source and reference point clouds through feature interaction, resulting in a richer feature representation. Second, based on the hybrid features, an overlapping mask decoding module is utilized to predict and sample all the overlapping points in the reference point cloud. Finally, using the feature of points in the overlapping area, a pose regression module is utilized to estimate the relative pose parameters of the two groups of point clouds. Experimental results show that the proposed method can significantly increase point cloud registration accuracy and has higher robustness against noise interference. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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