基于多频谱特征交互与动态融合的航天器6D位姿估计

童伟; 林茜; 严颖; 林金星; 李涛; 吴奇

doi:10.11999/JEIT260452

基于多频谱特征交互与动态融合的航天器6D位姿估计

doi: 10.11999/JEIT260452 cstr: 32379.14.JEIT260452

童伟^{1, 2, 3},
林茜⁴,
严颖^{1, 5, ,},
林金星²,
李涛¹,
吴奇⁶

1.
南京信息工程大学自动化学院南京市 210044
2.
南京邮电大学自动化学院南京市 210023
3.
社会计算与认知智能教育部重点实验室大连市 116024
4.
山西大学计算机与信息技术学院太原市 030031
5.
安徽建筑大学机械与电气工程学院合肥市 230009
6.
上海交通大学计算机学院上海市 200042

基金项目: 国家自然科学基金青年科学基金项目(62405145)，江苏省自然科学基金青年项目(BK20240641)，社会计算与认知智能教育部重点实验室开放基金(SCCI2023YB02)，南京信息工程大学人才启动经费，江苏高校教育信息化研究课题(2025JSETKT158)

详细信息

作者简介:
林金星：男，教授，研究方向为深度学习

李涛：男，教授，院长，研究方向为人机交互

吴奇：男，教授，研究方向为视脑力交互

通讯作者:
严颖　ying.yan@nuist.edu.cn

中图分类号: TP391
计量
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- 被引次数: 0
出版历程
- 修回日期: 2026-08-17
- 录用日期: 2026-08-17
- 网络出版日期: 2026-08-26

Multi-Frequency Feature Interaction and Adaptive Fusion for cooperative Spacecraft 6D Pose Estimation

TONG Wei^{1, 2, 3},
LIN Xi⁴,
YAN Ying^{1, 5
, ,},
LIN Jinxing²,
LI Tao¹,
WU Qi⁶

1.
School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, China
2.
School of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
3.
Key Laboratory of Social Computing and Cognitive Intelligence, Dalian 116024, China
4.
School of Computer and Information Technology, Shanxi University, Taiyuan 030031, China
5.
School of Mechanical and Electrical Engineering, Anhui Jianzhu University, Hefei 230009, China
6.
Computer College, Shanghai Jiaotong University, Shanghai 200042, China

Funds: National Natural Science Foundation of China(62405145), Natural Science Foundation of Jiangsu Province(BK20240641), Key Laboratory of Social Computing and Cognitive Intelligence(SCCI2023YB02) , Nanjing University of Information Science & Technology, Quality Assurance and Evaluation of higher Education in Jiangsu Province(2025JSETKT158)

摘要

摘要: 航天器的6自由度位姿估计，是指确定目标航天器相对于服务航天器在空间坐标系中的相对姿态的处理过程。该技术是失效卫星清理、太空垃圾捕获、航天器在轨操控、空间站交会对接等一系列近距离操作任务的关键步骤。近年来，基于CNN的6D位姿估计方法受到广泛关注，但其过度依赖卷积网络结构，容易导致对图像纹理的敏感，并缺乏对远程上下文信息的有效建模能力。此外，当前主流方法通常采用由目标检测与姿态估计构成的流水线设计，其特征提取多样性有限，对低光照和复杂背景干扰较为敏感。针对上述问题，本文提出一种基于多频谱特征交互与动态融合的航天器位姿估计网络。首先，该网络利用不同感受野的主干网络分别提取图像的空间高频特征(如语义与边缘细节)和空间低频特征(如全局结构信息)；在此基础上，基于Transformer的特征匹配机制，进行自特征与跨特征注意力交互，实现长范围上下文信息的聚合；为进一步利用丰富的频域特征表示，引入频率引导特征模块，动态融合多频谱特征。最后，在航天器位姿估计数据集上的大量实验表明，所提出方法具有竞争力，并展现了较强的泛化能力，相比于现有方法具有优势。
- 在轨操控 /
- 交会对接 /
- 位姿估计 /
- 特征交互 /
- 空间频率特征
Abstract: Spacecraft 6D pose estimation aims to determine the relative pose between the target spacecraft and the service spacecraft in the spatial coordinate system, which is a critical step for a series of close-range operation tasks, such as failed satellite cleaning, space debris capture, on-orbit spacecraft manipulation, and space station rendezvous and docking. In recent years, CNN-based 6D pose estimation methods have received widespread attention. However, their over-reliance on convolutional network architectures makes them sensitive to image textures and limits their capacity to effectively model long-range contextual information. Moreover, current mainstream methods typically adopt a pipeline design consisting of object detection followed by pose estimation, which suffers from limited diversity in feature extraction and is sensitive to low-light conditions and complex background interference. To address these issues, this paper proposes a spacecraft pose estimation network based on multi-spectral feature interaction and dynamic fusion. Specifically, the network first leverages backbone networks with different receptive fields to separately extract spatial high-frequency features (such as semantic and edge details) and spatial low-frequency features (such as global structural information). On this basis, a Transformer-based feature matching mechanism is employed to perform self-attention and cross-attention feature interactions, thereby aggregating long-range contextual information. To further exploit the rich frequency-domain feature representations, a frequency-guided feature module is introduced to dynamically fuse multi-spectral features. Finally, extensive experiments on spacecraft pose estimation datasets demonstrate that the proposed method achieves competitive performance and strong generalization ability, showing advantages over existing methods. Objective Due to the limitation of local receptive field of CNN convolution operator, the mining of remote correlation information is often not ideal. In contrast, Transformer with multi-head intra-attention mechanism is more effective in globally modeling long-range context information and is good at processing multi-scale objects and small spacecraft at a long distance. Therefore, enhancing the processing ability of low-frequency and high-frequency features is of great significance for improving the accuracy. Methods This work proposes a 6D pose estimation network with Transformer-based multi-frequency feature interaction and fusion. The framework consists of two parts. One is to extract high-frequency features such as image semantics and edges via ResNet18 and leverage them for spacecraft semantic segmentation directly. The other is to extract global low-frequency features such as image texture through DarkNet53. Then Transformer-based feature interaction module is designed to perform attention between high-frequency and low-frequency features, enabling the aggregation of long-range contextual information, which can enhance the diversity of texture-less spacecraft image features and promote network optimization. Results and Discussions The proposed method estimates spacecraft 6D pose via multi-frequency feature interaction and adaptive fusion. On the SwissCube dataset, it achieves an overall ADI-0.1d accuracy of 82.31%, outperforming CA-SpaceNet (79.39%) and WDR* (78.78%). On the SPEED dataset, the combined error eq+et is 0.0299, which is 22.3% lower than CA-SpaceNet (0.0385) and 25.2% lower than WDR* (0.0400). Qualitative results in Fig. 5 and Fig. 7 show that predicted key points are significantly closer to the ground truth, especially under challenging low-frequency conditions. Ablation studies confirm that the feature interaction module alone increases overall accuracy from 79.39% to 80.67%, and adding frequency-guided fusion further raises it to 82.31%. These results demonstrate that the proposed framework enhances pose estimation robustness and meets the stringent requirements for on-orbit servicing and space rendezvous. Conclusions To enhance the accuracy of spacecraft 6D pose estimation under extreme atmospheric environments, this work innovatively designs a sub-branch based on multi-frequency feature interaction and additional semantic edge segmentation, and overcomes the limitation of low feature extraction efficiency of existing methods by aggregating long-range multi-frequency features. In addition, a frequency-guided dynamic feature fusion module is introduced to fully leverage the rich frequency-domain feature representation. Comprehensive experimental comparison with mainstream methods on SwissCube and SPEED datasets verifies that the proposed work can enhance the representation of feature information and improve the robustness of spacecraft pose estimation.
- On-orbit control /
- Rendezvous and Docking /
- Pose estimation /
- Feature interaction /
- Multi-spectral features

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图 1 所提出的基于Transformer多频谱特征交互的航天器6D位姿估计网络结构

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图 2 基于PnP特征点匹配的位姿估计示意图

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图 3 基于多频谱特征交互的网络结构示意图。对高频语义边缘特征、低频特征分别进行位置编码与平坦化，然后对二类特征进行内注意与间注意

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图 4 频率引导的多频谱特征融合结构示意图

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图 5 本文方法、WDR*以及CA-SpaceNet生成的预测关键点的可视化。为了清晰起见，Ground truth用红色框表示，所有预测点都用黄色标记。本文方法预测的姿态估计结果更加准确

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图 6 Resnet18分支和DarkNet分支的层次特征图和能谱值可视化

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图 7 本文方法与基准方法所生成的预测关键点的可视化。为了清晰起见，Ground truth用绿色框表示，所有预测点都用红色标记

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图 8 与基准方法在训练阶段的ADI 0.1d对比

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图 9 不同模型组合方法预测关键点在2D与3D投影的误差对比

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表 1 与主流方法在SWISSCUBE数据集的定量比较结果

Method	Near ↑‌	Medium ↑‌	Far ↑‌	All ↑‌
SegDriven^[26]	41.1	22.9	7.1	21.8
SegDriven-Z^[26]	52.6	45.4	29.4	43.2
DLR^[17]	63.8	47.8	28.9	46.8
WDR	65.2	48.7	31.9	47.9
WDR*	92.37	84.16	61.27	78.78
CA-SpaceNet	91.01	86.32	61.72	79.39
DTSE-SpaceNet	92.57	88.74	64.42	81.65
WDR +本文方法	96.24	88.82	63.13	81.09
CA-SpaceNet+本文方法	95.76	90.61	65.12	82.31

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表 2 在SPEED数据集的交叉验证定量比较结果

Metric	1	2	3	4	5	mean	std
Mean$ {e}_{q} $$ {\mathrm{e}}_{\mathrm{q}} $	0.023058	0.022846	0.022311	0.023072	0.022629	0.022783	0.000286
Median$ {e}_{q} $	0.017626	0.017660	0.017817	0.017887	0.017896	0.017772	0.000113
Mean$ {e}_{t} $	0.007490	0.007149	0.007460	0.007267	0.007178	0.007308	0.000141
Median $ {e}_{t} $	0.005309	0.005074	0.004966	0.005180	0.005117	0.005129	0.000114
Mean S	0.030548	0.029995	0.029771	0.030339	0.029807	0.030092	0.000304
Median S	0.022935	0.022734	0.022783	0.023067	0.023013	0.0229064	0.000128

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表 3 与主流方法在SPEED数据集的定量比较结果

模型	$ {\mathrm{e}}_{\mathrm{q}}+{\mathrm{e}}_{\mathrm{t}} $
SLAB Baseline^[27]	0.0626
pedro-fairspace^[11]	0.0571
WDR*	0.0400
CA-SpaceNet	0.0385
本文方法	0.0299

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表 4 不同模型组合方法在SWISSCUBE数据集的定量比较结果

Method	Near ↑‌	Medium ↑‌	Far ↑‌	All ↑‌
CA-SpaceNet	91.01	86.32	61.72	79.39
CA-SpaceNet + Transformer特征交互	95.02	88.20	63.49	80.67
CA-SpaceNet + Transformer特征交互	(+4.01)	(+1.88)	(+1.77)	(+1.28)
本文方法	95.76	90.61	65.12	82.31
本文方法	(+4.65)	(+4.29)	(+3.40)	(+2.92)

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表 5 与主流方法的模型参数定量比较结果

模型	模型参数	模型大小
WDR*	52.1M	205.2MB
CA-SpaceNet	51.3M	205.17MB
DTSE-SpaceNet	51.9M	206.2MB
本文方法	62.4M	249.6MB

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