首页|Feature-aided pose estimation approach based on variational auto-encoder structure for spacecrafts

Feature-aided pose estimation approach based on variational auto-encoder structure for spacecrafts

扫码查看
原文链接
  • NETL
  • NSTL
  • 万方数据
Real-time 6 Degree-of-Freedom(DoF)pose estimation is of paramount importance for various on-orbit tasks.Benefiting from the development of deep learning,Convolutional Neural Networks(CNNs)in feature extraction has yielded impressive achievements for spacecraft pose estimation.To improve the robustness and interpretability of CNNs,this paper proposes a Pose Estimation approach based on Variational Auto-Encoder structure(PE-VAE)and a Feature-Aided pose estimation approach based on Variational Auto-Encoder structure(FA-VAE),which aim to accurately estimate the 6 DoF pose of a target spacecraft.Both methods treat the pose vector as latent variables,employing an encoder-decoder network with a Variational Auto-Encoder(VAE)structure.To enhance the precision of pose estimation,PE-VAE uses the VAE structure to intro-duce reconstruction mechanism with the whole image.Furthermore,FA-VAE enforces feature shape constraints by exclusively reconstructing the segment of the target spacecraft with the desired shape.Comparative evaluation against leading methods on public datasets reveals similar accuracy with a threefold improvement in processing speed,showcasing the significant contribution of VAE structures to accuracy enhancement,and the additional benefit of incorporating global shape prior features.

Pose estimationVariational auto-encoderFeature-aided Pose Estima-tion ApproachOn-orbit measurement tasksSimulated and experimental dataset

Yanfang LIU、Rui ZHOU、Desong DU、Shuqing CAO、Naiming QI

展开 >

Department of Aerospace Engineering,Harbin Institute of Technology,Harbin 150001,China

Suzhou Research Institute of HIT,Suzhou 215104,China

Shanghai Institute of Spaceflight Control Technology,Shanghai 201109,China

Shanghai Key Laboratory of Aerospace Intelligent Control Technology,Shanghai 201109,China

展开 >

National Natural Science Foundation of ChinaNatural Science Foundation of Heilongjiang Province of ChinaShanghai Sailing Program,China

52272390YQ2022A00920YF1417300

2024

10.1016/j.cja.2024.03.017

中国航空学报(英文版)
中国航空学会

中国航空学报(英文版)

CSTPCDEI
影响因子：0.847
ISSN：1000-9361
年,卷(期)：2024.37(8)