GKNet：用于非合作航天器单目位姿估计的基于图的关键点网络
GKNet: Graph-based Keypoints Network for Monocular Pose Estimation of Non-cooperative Spacecraft
AI总结：
提出基于图的关键点网络GKNet，利用关键点图的几何约束解决非合作航天器单目位姿估计中结构对称和部分遮挡问题，并构建了含3个目标9万张图像的SKD数据集，实验证明其高精度和有效性。
摘要：
非合作航天器的单目位姿估计对在轨服务（OOS）任务（如卫星维护、空间碎片清除和空间站组装）具有重要意义。考虑到对位姿估计精度的高要求，主流的单目位姿估计方法通常由关键点检测器和PnP求解器组成。然而，当前的关键点检测器仍然容易受到非合作航天器结构对称和部分遮挡的影响。为此，我们提出了一种基于图的关键点网络GKNet，用于非合作航天器的单目位姿估计，该网络利用了关键点图的几何约束。为了更好地验证关键点检测器，我们提出了一个中等规模的航天器关键点检测数据集SKD，包含3个航天器目标、90,000张模拟图像及对应的高精度关键点标注。大量实验和消融研究表明，与最先进的航天器关键点检测器相比，我们的GKNet具有高精度和有效性。GKNet的代码和SKD数据集可在 https://github.com/Dongzhou-1996/GKNet 获取。
英文摘要：
Monocular pose estimation of non-cooperative spacecraft is significant for on-orbit service (OOS) tasks, such as satellite maintenance, space debris removal, and station assembly. Considering the high demands on pose estimation accuracy, mainstream monocular pose estimation methods typically consist of keypoint detectors and PnP solver. However, current keypoint detectors remain vulnerable to structural symmetry and partial occlusion of non-cooperative spacecraft. To this end, we propose a graph-based keypoints network for the monocular pose estimation of non-cooperative spacecraft, GKNet, which leverages the geometric constraint of keypoints graph. In order to better validate keypoint detectors, we present a moderate-scale dataset for the spacecraft keypoint detection, named SKD, which consists of 3 spacecraft targets, 90,000 simulated images, and corresponding high-precise keypoint annotations. Extensive experiments and an ablation study have demonstrated the high accuracy and effectiveness of our GKNet, compared to the state-of-the-art spacecraft keypoint detectors. The code for GKNet and the SKD dataset is available at https://github.com/Dongzhou-1996/GKNet.
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。
