PA-FlowNet: Pose-auxiliary optical flow network for spacecraft relative pose estimation

Zhi Yu Chen, Po Heng Chen, Kuan-Wen Chen, Chen Yu Chan

研究成果: Conference contribution同行評審

2 引文 斯高帕斯(Scopus)

摘要

During the process of space travelling and space landing, the spacecraft attitude estimation is the indispensable work for navigation. Since there are not enough satellites for GPS-like localization in space, the computer vision technique is adopted to address the issue. The most crucial task for localization is the extraction of correspondences. In computer vision, optical flow estimation is often used for finding correspondences between images. As the deep neural network being more popular in recent years, FlowNet2 has played a vital role which achieves great success. In this paper, we present PA-FlowNet, an end-to-end pose-auxiliary optical flow network which can use the predicted relative camera pose to improve the performance of optical flow. PA-FlowNet is composed of two sub-networks, the foreground-attention flow network and the pose regression network. The foreground-attention flow network is constructed by FlowNet2 model and modified with the proposed foreground-attention approach. We introduced this approach with the concept of curriculum learning for foreground-background segmentation to avoid backgrounds from resulting in flow prediction error. The pose regression network is used to regress the relative camera pose as an auxiliary for increasing the accuracy of the flow estimation. In addition, to simulate the test environment for spacecraft pose estimation, we construct a 64K moon model and simulate aerial photography with various attitudes to generate Moon64K dataset in this paper. PA-FlowNet significantly outperforms all existing methods on the proposed Moon64K dataset. Furthermore, we also predict the relative camera pose via proposed PA-FlowNet and accomplish the remarkable performance.

原文English
主出版物標題Proceedings of ICPR 2020 - 25th International Conference on Pattern Recognition
發行者Institute of Electrical and Electronics Engineers Inc.
頁面9703-9710
頁數8
ISBN(電子)9781728188089
DOIs
出版狀態Published - 2020
事件25th International Conference on Pattern Recognition, ICPR 2020 - Virtual, Milan, Italy
持續時間: 10 1月 202115 1月 2021

出版系列

名字Proceedings - International Conference on Pattern Recognition
ISSN(列印)1051-4651

Conference

Conference25th International Conference on Pattern Recognition, ICPR 2020
國家/地區Italy
城市Virtual, Milan
期間10/01/2115/01/21

指紋

深入研究「PA-FlowNet: Pose-auxiliary optical flow network for spacecraft relative pose estimation」主題。共同形成了獨特的指紋。

引用此