SESPnet: a lightweight network with attention mechanism for spacecraft pose estimation

被引：0

作者：

Chen C. ^{[1
]}

Jing Z. ^{[1
]}

Pan H. ^{[1
]}

Dun X. ^{[1
]}

Huang J. ^{[1
]}

Wu H. ^{[2
]}

Cao S. ^{[2
]}

机构：

[1] School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai

[2] Shanghai Aerospace Control Technology, Shanghai

来源：

Aerospace Systems | 2024年 / 7卷 / 01期

基金：

中国国家自然科学基金;

关键词：

Attention mechanism; Convolutional neural network; Embedded application; Spacecraft pose estimation;

D O I：

10.1007/s42401-023-00259-w

中图分类号：

学科分类号：

摘要：

Spacecraft pose estimation plays an important role in an increasing number of on-orbit services: rendezvous and docking, formation flights, debris removal, and so on. Current solutions achieve excellent performance at the cost of a huge number of model parameters and are not applicable in space environments where computational resources are limited. In this paper, we present the Squeeze-and-Excitation based Spacecraft Pose Network (SESPNet). Our primary objective is to make a trade-off between minimizing model parameters and preserving performance to be more applicable to edge computing in space environments. Our contributions are primarily manifested in three aspects: first, we adapt the lightweight PeleeNet as the backbone network; second, we incorporate the SE attention mechanism to bolster the network’s feature extraction capabilities; third, we adopt the Smooth L1 loss function for position regression, which significantly enhances the accuracy of position estimation. © Shanghai Jiao Tong University 2023.

引用

页码：1 / 10

页数：9

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