GKNet通过图结构约束提升非合作航天器单目位姿估计精度
GKNet: Graph-based Keypoints Network for Monocular Pose Estimation of Non-cooperative Spacecraft
- 基于关键点图的几何约束设计新网络结构
- 在3个航天器、9万张图像上实现高精度检测
- 适合航天器视觉伺服与在轨服务任务研究者
非合作航天器的单目位姿估计对在轨服务（如卫星维护、空间碎片清除、空间站组装）具有重要意义。主流方法通常由关键点检测器和PnP求解器构成，但现有检测器仍易受结构对称性和部分遮挡影响。为此，本文提出GKNet——一种基于关键点图几何约束的单目位姿估计方法。为更好评估关键点检测器，我们构建了一个中等规模的数据集SKD，包含3个航天器目标、90,000张模拟图像及高精度关键点标注。大量实验与消融研究证明，GKNet在精度和有效性上优于当前最先进方法。代码与数据集已开源。
原文摘要 · Abstract (English)
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.
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