VIPE:可见和红外融合的空间非合作物体的位置估计框架
Zhao Zhang1, Dong Zhou1, Yuhui Hu1
1Department of Control Science and Engineering, Harbin Institute of Technology, Harbin 150001, China.
Sensors (Basel, Switzerland)
|November 13, 2025
概括
通过融合可见和红外图像,提高了对非合作空间物体的准确姿势估计. 这种新的深度学习框架,VIPE,增强了卫星维护和在低可见度情况下的碎片清除能力.
科学领域:
- 太空飞船工程 太空飞船工程
- 计算机视觉 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
背景情况:
- 对非合作性太空物体进行准确的姿势估计对于太空任务,如垃圾清除和卫星维修至关重要.
- 现有的单眼姿势估计方法在可见度低的空间环境中扎.
研究的目的:
- 开发一种新的深度学习框架,用于使用融合可见和红外图像准确地估计非合作空间物体的位置.
- 在具有挑战性的空间条件下解决单眼方法的局限性.
主要方法:
- 提出了一个可见和红外融合姿势估计框架 (VIPE),集成一个图像融合子网络和一个姿势估计子网络.
- 聚变子网络将可见和红外图像的多尺度特征合并在一起.
- 开发了一个新的双模视觉姿势估计 (BVPE) 数据集,包含3630对可见红外图像对.
主要成果:
- VIPE框架显著优于现有的单眼姿势估计方法.
- 在复杂和低可见度的空间环境中表现出卓越的性能.
- 实现了更可靠,更准确的姿势估计结果.
结论:
- 拟议的VIPE框架有效地将可见和红外图像融合在一起,以进行对非合作空间物体的稳健姿势估计.
- BVPE数据集为推进该领域的研究提供了宝贵的资源.
- 对于需要精确物体定位的关键空间操作,VIPE提供了一个有前途的解决方案.
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