Vision based reconstruction and pose estimation for spacecraft with axisymmetric structure

Wu, Bin; Xie, Lei; Cai, Yifei; Li, Lei

doi:10.1016/j.ijleo.2020.164945

Cited by 3 publications

(4 citation statements)

References 9 publications

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“…The measurement based on neural network is optimized by using ResNet34 (the number of ResNet layers is 34) with momentum of 0.9, weight attenuation factor of 0.01, characteristic channels of 90, and batch size of 4 to insure the correct convergence of the network. And the traditional pose measurement in table 5 is based on the method in [22].…”

Section: Verification Of Pose Estimation In Ground Test Systemmentioning

confidence: 99%

“…Set the camera frame rate to 20 fps, that is, the exposure time of each frame image is 50 ms. Through statistical analysis, in the image input of different illumination, background and target motion changes, the neural network algorithm in this paper and the algorithm in [22] are used to record the whole process time of pose estimation. The statistical data of processing time are shown in table 6.…”

Section: Verification Of Pose Estimation In Ground Test Systemmentioning

confidence: 99%

“…The comparison of time statistics in the whole process of pose estimation by using the neural network algorithm in this paper and the algorithm in[22].…”

mentioning

confidence: 99%

“…Relative error ratios of each axis at different relative distances in actual experiments of the ground test system based on traditional pose estimation algorithm with Ref [22]…”

mentioning

confidence: 99%

See 3 more Smart Citations

Influence factors of the accuracy of monocular vision pose estimation for spacecraft based on neural network

Zhao¹,

Ye²,

Guo³

et al. 2022

Eng. Res. Express

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Accurate acquisition of spacecraft pose information is a key step in tasks such as space debris removal, rendezvous and docking, and on-orbit maintenance. The application of neural network has deepened for aerospace measurement and control. Studying the parameters’ influence and degree of imaging and neural network on the accuracy of spacecraft pose estimation is necessary and urgent, but there is still little research in related fields. Approximate Bayesian inference in the depth Gaussian process is utilized in this paper to explore above problems, and systematic verification experiments are carried based on the general neural network and spacecraft pose estimation data set. The verification results depict that the estimation errors of position and attitude can be both effectively reduced by selecting a camera whose radial distortion error is less than 240%. Selecting a camera with focal ratio error less than 2.0% can effectively reduce the position estimation error. Selecting a camera whose principal point error is less than 1.0% can effectively improve the accuracy of attitude estimation. The suppression of tangential distortion error does not significantly improve the estimation accuracy of both position and attitude. Parameters about weighted attenuation factor, the number of network layers and characteristic channels of neural network are all sensitive to the accuracy of pose estimation of spacecraft within a specific range. An excellent balance can be achieved between the estimation accuracy and computational overhead when the weight attenuation factor is set between 0.01 and 0.1, and the number of network layers and characteristic channels are set separately between 20~40 and 70~90. It can reach the accuracy of the traditional pose estimation algorithm, and has better processing time stability in real ground test system by using the network architecture of ResNet34 with weight attenuation factor of 0.01, characteristic channels of 90.

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Section: Verification Of Pose Estimation In Ground Test Systemmentioning

confidence: 99%

Section: Verification Of Pose Estimation In Ground Test Systemmentioning

confidence: 99%

“…The comparison of time statistics in the whole process of pose estimation by using the neural network algorithm in this paper and the algorithm in[22].…”

mentioning

confidence: 99%

“…Relative error ratios of each axis at different relative distances in actual experiments of the ground test system based on traditional pose estimation algorithm with Ref [22]…”

mentioning

confidence: 99%

See 2 more Smart Citations

Influence factors of the accuracy of monocular vision pose estimation for spacecraft based on neural network

Zhao¹,

Ye²,

Guo³

et al. 2022

Eng. Res. Express

View full text Add to dashboard Cite

show abstract

Review on key technologies of space intelligent grasping robot

Yang

Chang

2022

J Braz. Soc. Mech. Sci. Eng.

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Monocular Depth Estimation of Noncooperative Spacecraft Based on Deep Learning

Zhao

Zhang

Gao

2023

Journal of Aerospace Information Systems

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Estimating position and stereographic geometry information of noncooperative spacecraft through three-dimensional (3-D) reconstruction techniques is of great significance. Depth information acquisition is an important component of 3-D reconstruction. Monocular cameras are cheaper and more widely used than depth sensors. The relative positions of noncooperative spacecraft and our spacecraft can be calculated from depth maps of monocular depth estimation and the camera parameters, providing data support for subsequent tracking and capture missions. This paper proposes a monocular depth estimation network combining the convolutional neural network (CNN) and a vision transformer (VIT) to improve the prediction accuracy of few-shot samples. We extract detail features and global features from the CNN and VIT encoders, respectively, and then fuse deep features and shallow features by a skip-connected upsampling decoder. Compared with the representative depth estimation algorithms in recent years on the NYU-Depth V2 dataset, the proposed network structure combines the advantages of the CNN and VIT as well as estimates the global depth of the scene more accurately while maintaining details. To solve the lack of spacecraft data collection, a new dataset is made from 3-D simulation models. Experiments on the self-made dataset demonstrate the feasibility of this method in aerospace engineering.

show abstract

Vision based reconstruction and pose estimation for spacecraft with axisymmetric structure

Cited by 3 publications

References 9 publications

Influence factors of the accuracy of monocular vision pose estimation for spacecraft based on neural network

Influence factors of the accuracy of monocular vision pose estimation for spacecraft based on neural network

Review on key technologies of space intelligent grasping robot

Monocular Depth Estimation of Noncooperative Spacecraft Based on Deep Learning

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