用于非合作航天器6D位姿估计的跨模态RGB-D融合Transformer
Cross-Modal RGB-D Fusion Transformer for 6D Pose Estimation of Non-Cooperative Spacecraft with Stereo-Derived Depth
AI总结：
本文提出基于被动立体视觉的6D位姿估计方法，通过TSCA-Stereo网络处理空间图像中的弱纹理和强光问题，并结合跨模态Transformer融合RGB和立体深度信息，实现高精度位姿估计。
摘要：
在轨服务和主动清除非合作航天器需要可靠的位姿估计以提供准确的位置和姿态数据用于自主视觉导航。基于学习的单目方法在航天器位姿估计中广泛应用，但存在固有的深度模糊问题且在轨道上常见的恶劣光照条件下容易失效。主动深度传感器理论上可以解决几何模糊问题，但其功率和质量要求使其不适合大多数航天平台。本文通过被动立体视觉框架解决这些问题，开发了TSCA-Stereo立体匹配网络以应对弱纹理表面、镜面高光和严重光照变化。引入了跨模态融合Transformer，以适应性方式结合RGB外观信息与立体深度特征，支持可靠的位姿恢复。还构建了一个合成双目多模态数据集用于实验，涵盖立体视差图和6-DOF位姿注释，覆盖多种光照场景、姿态配置和噪声水平。实验结果表明，TSCA-Stereo在该空间专用数据集上优于基线方法。完整的位姿估计管道在不同成像条件下实现了平均位移误差0.0419米和平均姿态误差0.8632度，证实了被动立体方法在严苛空间视觉条件下的有效性和鲁棒性。
英文摘要：
On-orbit servicing and active debris removal involving non-cooperative spacecraft require reliable pose estimation to supply accurate position and orientation data for autonomous visual navigation. Learning-based monocular methods have seen widespread adoption in spacecraft pose estimation, yet they suffer from an intrinsic depth ambiguity problem and tend to fail under the harsh illumination conditions routinely encountered in orbit. Active depth sensors could in principle address the geometric ambiguity, but their power and mass requirements make them poorly suited to most spacecraft platforms. This work addresses these issues through a passive stereo vision framework for six-degree-of-freedom (6-DOF) pose estimation of non-cooperative spacecraft. A binocular stereo matching network called TSCA-Stereo is developed to cope with weak-texture surfaces, specular highlights, and severe lighting variations typical of space imagery. A cross-modal fusion Transformer is introduced to combine RGB appearance information with stereo depth features in an adaptive manner, supporting reliable pose recovery. A synthetic binocular multimodal dataset is also built for the experiments, covering stereo disparity maps and 6-DOF pose annotations across a range of illumination scenarios, attitude configurations, and noise levels. Experimental results show that TSCA-Stereo outperforms the baseline across every evaluated metric on this space-specific dataset. The full pose estimation pipeline achieves a mean translation error of 0.0419 m and a mean orientation error of 0.8632° under varied imaging conditions, confirming that the passive stereo approach is both effective and resilient when operating under the demanding visual conditions of the space environment.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
