An Efficient Pose Estimation Algorithm for Non-Cooperative Space Objects Based on Dual-Channel Transformer

Non-cooperative space object pose estimation is a key technique for spatial on-orbit servicing, where pose estimation algorithms based on low-quality, low-power monocular sensors provide a practical solution for spaceborne applications. The current pose estimation methods for non-cooperative space o...

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Main Authors: Ruida Ye, Yuan Ren, Xiangyang Zhu, Yujing Wang, Mingyue Liu, Lifen Wang
Format: Article
Language:English
Published: MDPI AG 2023-11-01
Series:Remote Sensing
Subjects:
Online Access:https://www.mdpi.com/2072-4292/15/22/5278
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author Ruida Ye
Yuan Ren
Xiangyang Zhu
Yujing Wang
Mingyue Liu
Lifen Wang
author_facet Ruida Ye
Yuan Ren
Xiangyang Zhu
Yujing Wang
Mingyue Liu
Lifen Wang
author_sort Ruida Ye
collection DOAJ
description Non-cooperative space object pose estimation is a key technique for spatial on-orbit servicing, where pose estimation algorithms based on low-quality, low-power monocular sensors provide a practical solution for spaceborne applications. The current pose estimation methods for non-cooperative space objects using monocular vision generally consist of three stages: object detection, landmark regression, and perspective-n-point (PnP) solver. However, there are drawbacks, such as low detection efficiency and the need for prior knowledge. To solve the above problems, an end-to-end non-cooperative space object pose estimation learning algorithm based on dual-channel transformer is proposed, a feature extraction backbone network based on EfficientNet is established, and two pose estimation subnetworks based on transformer are also established. A quaternion SoftMax-like activation function is designed to improve the precision of orientation error estimating. The method only uses RGB images, eliminating the need for a CAD model of the satellite, and simplifying the detection process by using an end-to-end network to directly detect satellite pose information. Experiments are carried out on the SPEED dataset provided by the European Space Agency (ESA). The results show that the proposed algorithm can successfully predict the satellite pose information and effectively decouple the spatial translation information and orientation information, which significantly improves the recognition efficiency compared with other methods.
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spelling doaj.art-bed2fa9dd8914feab7eee9b58ab1af6c2023-11-24T15:04:09ZengMDPI AGRemote Sensing2072-42922023-11-011522527810.3390/rs15225278An Efficient Pose Estimation Algorithm for Non-Cooperative Space Objects Based on Dual-Channel TransformerRuida Ye0Yuan Ren1Xiangyang Zhu2Yujing Wang3Mingyue Liu4Lifen Wang5Department of Aerospace Engineering and Technology, Space Engineering University, Beijing 101416, ChinaDepartment of Basic Course, Space Engineering University, Beijing 101416, ChinaDepartment of Aerospace Engineering and Technology, Space Engineering University, Beijing 101416, ChinaDepartment of Basic Course, Space Engineering University, Beijing 101416, ChinaDepartment of Aerospace Engineering and Technology, Space Engineering University, Beijing 101416, ChinaDepartment of Aerospace Engineering and Technology, Space Engineering University, Beijing 101416, ChinaNon-cooperative space object pose estimation is a key technique for spatial on-orbit servicing, where pose estimation algorithms based on low-quality, low-power monocular sensors provide a practical solution for spaceborne applications. The current pose estimation methods for non-cooperative space objects using monocular vision generally consist of three stages: object detection, landmark regression, and perspective-n-point (PnP) solver. However, there are drawbacks, such as low detection efficiency and the need for prior knowledge. To solve the above problems, an end-to-end non-cooperative space object pose estimation learning algorithm based on dual-channel transformer is proposed, a feature extraction backbone network based on EfficientNet is established, and two pose estimation subnetworks based on transformer are also established. A quaternion SoftMax-like activation function is designed to improve the precision of orientation error estimating. The method only uses RGB images, eliminating the need for a CAD model of the satellite, and simplifying the detection process by using an end-to-end network to directly detect satellite pose information. Experiments are carried out on the SPEED dataset provided by the European Space Agency (ESA). The results show that the proposed algorithm can successfully predict the satellite pose information and effectively decouple the spatial translation information and orientation information, which significantly improves the recognition efficiency compared with other methods.https://www.mdpi.com/2072-4292/15/22/5278non-cooperative space objectspose estimationmonocular sensordual-channel transformerquaternion SoftMax-like activation function
spellingShingle Ruida Ye
Yuan Ren
Xiangyang Zhu
Yujing Wang
Mingyue Liu
Lifen Wang
An Efficient Pose Estimation Algorithm for Non-Cooperative Space Objects Based on Dual-Channel Transformer
Remote Sensing
non-cooperative space objects
pose estimation
monocular sensor
dual-channel transformer
quaternion SoftMax-like activation function
title An Efficient Pose Estimation Algorithm for Non-Cooperative Space Objects Based on Dual-Channel Transformer
title_full An Efficient Pose Estimation Algorithm for Non-Cooperative Space Objects Based on Dual-Channel Transformer
title_fullStr An Efficient Pose Estimation Algorithm for Non-Cooperative Space Objects Based on Dual-Channel Transformer
title_full_unstemmed An Efficient Pose Estimation Algorithm for Non-Cooperative Space Objects Based on Dual-Channel Transformer
title_short An Efficient Pose Estimation Algorithm for Non-Cooperative Space Objects Based on Dual-Channel Transformer
title_sort efficient pose estimation algorithm for non cooperative space objects based on dual channel transformer
topic non-cooperative space objects
pose estimation
monocular sensor
dual-channel transformer
quaternion SoftMax-like activation function
url https://www.mdpi.com/2072-4292/15/22/5278
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