Abstract:  Booster separation is one of the important actions in the launching process of the carrier rocket. The commonly used LiDAR pose measurement technology is severely affected by external factors during the separation stage for the booster,so it is difficult to accurately obtain the pose of booster. To improve the anti-interference ability of pose estimation,the vision-based pose measurement technology was utilized for booster. A 3D point cloud reconstruction network whose input was the image and output was corresponding 3D point cloud was built and trained on the imagepoint cloud dataset,which was constructed during the separation of booster. During testing,the pose estimation was completed via principal component analysis on the reconstructed booster point cloud. All the experimental results illustrate that pose changes can be measured precisely by the built network according to the simulation image data during the separation stage for booster. Under the R2score metric,the prediction scores for the three-dimensional coordinates are all above 0.98.For the attitude angle,the average error is about 21°,and the prediction scores are all above 0.80.

Key words: neural networks, 3D point cloud reconstruction, generative model, principal component analysis, pose estimation