In recent years, there has been a growing demand for improved autonomy for in-orbit operations such as rendezvous, docking, and proximity maneuvers, leading to increased interest in employing Deep Learning-based Spacecraft Pose Estimation techniques. However, due to limited access to real target datasets, algorithms are often trained using synthetic data and applied in the real domain, resulting in a performance drop due to the domain gap. State-of-the-art approaches employ Domain Adaptation techniques to mitigate this issue. In the search for viable solutions, event sensing has been explored in the past and shown to reduce the domain gap between simulations and real-world scenarios. Event sensors have made significant advancements in hardware and software in recent years. Moreover, the characteristics of the event sensor offer several advantages in space applications compared to RGB sensors. To facilitate further training and evaluation of DL-based models, we introduce a novel dataset, SPADES, comprising real event data acquired in a controlled laboratory environment and simulated event data using the same camera intrinsics. Furthermore, we propose an effective data filtering method to improve the quality of training data, thus enhancing model performance. Additionally, we introduce an image-based event representation that outperforms existing representations. A multifaceted baseline evaluation was conducted using different event representations, event filtering strategies, and algorithmic frameworks, and the results are summarized. The dataset will be made available at http://cvi2.uni.lu/spades.

翻译：近年来，随着对交会对接、近距离机动等在轨操作自主性提升的需求日益增长，基于深度学习的航天器姿态估计技术受到广泛关注。然而，由于真实目标数据获取受限，算法通常依赖合成数据训练并应用于真实场景，导致领域差距引发性能下降。目前主流方法采用领域自适应技术缓解该问题。在探索可行方案的过程中，事件传感技术已被证实可有效缩小仿真环境与真实场景之间的领域差距。近年来，事件传感器在硬件与软件层面均取得显著进展，相较于RGB传感器，其特性在航天应用中展现出多项优势。为促进基于深度学习的模型训练与评估，我们提出一种新型数据集SPADES，该数据集包含在受控实验环境下采集的真实事件数据，以及使用相同相机内参生成的仿真事件数据。此外，我们提出一种有效的数据滤波方法以提升训练数据质量，进而增强模型性能。同时，我们引入一种基于图像的事件表征方法，其性能优于现有表征方式。通过采用不同事件表征、事件滤波策略及算法框架的多维度基线评估，本文总结了实验结果。该数据集将在http://cvi2.uni.lu/spades开放获取。