Bridging the Domain Gap in Satellite Pose Estimation: A Self-Training Approach Based on Geometrical Constraints

Wang, Zi; Chen, Nanjun; Guo, Yulan; Li, Zhang; Yu, Qifeng

doi:10.1109/taes.2023.3250385

Cited by 7 publications

(1 citation statement)

References 61 publications

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“…SPEC2021 particularly emphasizes domain gaps where the source and target data distributions differ, while the tasks remain the same. We adopt a semi-supervised learning strategy [ 37 , 38 , 39 ]. Specifically, we randomly select a portion of the target dataset and annotate it in the format of coco [ 34 ].…”

Section: Methodsmentioning

confidence: 99%

Spacecraft Homography Pose Estimation with Single-Stage Deep Convolutional Neural Network

Chen,

Yang,

Wang

et al. 2024

Sensors

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Spacecraft pose estimation using computer vision has garnered increasing attention in research areas such as automation system theory, control theory, sensors and instruments, robot technology, and automation software. Confronted with the extreme environment of space, existing spacecraft pose estimation methods are predominantly multi-stage networks with complex operations. In this study, we propose an approach for spacecraft homography pose estimation with a single-stage deep convolutional neural network for the first time. We formulated a homomorphic geometric constraint equation for spacecraft with planar features. Additionally, we employed a single-stage 2D keypoint regression network to obtain homography 2D keypoint coordinates for spacecraft. After decomposition to obtain the rough spacecraft pose based on the homography matrix constructed according to the geometric constraint equation, a loss function based on pixel errors was employed to refine the spacecraft pose. We conducted extensive experiments using widely used spacecraft pose estimation datasets and compared our method with state-of-the-art techniques in the field to demonstrate its effectiveness.

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Section: Methodsmentioning

confidence: 99%

Spacecraft Homography Pose Estimation with Single-Stage Deep Convolutional Neural Network

Chen,

Yang,

Wang

et al. 2024

Sensors

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A comprehensive study on PnP-based pipeline for pose estimation of noncooperative satellite

Yu,

Wang,

et al. 2024

Acta Astronautica

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SU-Net: pose estimation network for non-cooperative spacecraft on-orbit

Shen

Wang

et al. 2023

Sci Rep

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The estimation of spacecraft pose is crucial in numerous space missions, including rendezvous and docking, debris removal, and on-orbit maintenance. Estimating the pose of space objects is significantly more challenging than that of objects on Earth, primarily due to the widely varying lighting conditions, low resolution, and limited amount of data available in space images. Our main proposal is a new deep learning neural network architecture, which can effectively extract orbiting spacecraft features from images captured by inverse synthetic aperture radar (ISAR) for pose estimation of non-cooperative on orbit spacecraft. Specifically, our model enhances spacecraft imaging by improving image contrast, reducing noise, and using transfer learning to mitigate data sparsity issues via a pre-trained model. To address sparse features in spacecraft imaging, we propose a dense residual U-Net network that employs dense residual block to reduce feature loss during downsampling. Additionally, we introduce a multi-head self-attention block to capture more global information and improve the model’s accuracy. The resulting tightly interlinked architecture, named as SU-Net, delivers strong performance gains on pose estimation by spacecraft ISAR imaging. Experimental results show that we achieve the state of the art results, and the absolute error of our model is 0.128$$^{\circ }$$ ∘ to 0.4491$$^{\circ }$$ ∘ , the mean error is about 0.282$$^{\circ }$$ ∘ , and the standard deviation is about 0.065$$^{\circ }$$ ∘ . The code are released at https://github.com/Tombs98/SU-Net.

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Bridging the Domain Gap in Satellite Pose Estimation: A Self-Training Approach Based on Geometrical Constraints

Cited by 7 publications

References 61 publications

Spacecraft Homography Pose Estimation with Single-Stage Deep Convolutional Neural Network

Spacecraft Homography Pose Estimation with Single-Stage Deep Convolutional Neural Network

A comprehensive study on PnP-based pipeline for pose estimation of noncooperative satellite

SU-Net: pose estimation network for non-cooperative spacecraft on-orbit

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