SPADES：一个采用事件感知的真实航天器位姿估计数据集
SPADES: A Realistic Spacecraft Pose Estimation Dataset using Event Sensing
AI总结：
针对航天器位姿估计中合成数据与真实数据存在域差距导致模型性能下降的问题，研究团队推出包含真实与仿真事件数据的SPADES数据集，提出有效数据过滤方法和更优的图像类事件表示，并完成多维度基线评估。
摘要：
近年来，交会、对接及近距机动等在轨操作对自主性提升的需求日益增长，使得基于深度学习的航天器位姿估计技术受到越来越多的关注。然而，由于真实目标数据集获取受限，算法通常使用合成数据训练后应用于真实场景，域差距会导致性能下降。当前最优方法采用域适应技术来缓解这一问题。在探索可行方案的过程中，事件感知此前已被研究，且被证明能够缩小仿真与真实场景之间的域差距。近年来事件传感器在硬件和软件方面均取得了显著进展。此外，与RGB传感器相比，事件传感器的特性在航天应用中具备多项优势。为推动基于深度学习的模型的进一步训练与评估，我们推出了全新数据集SPADES，其包含在受控实验室环境中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，我们提出了一种有效的数据过滤方法，用于提升训练数据质量，进而增强模型性能。我们还提出了一种基于图像的事件表示方法，其性能优于现有表示方法。我们使用不同的事件表示、事件过滤策略及算法框架开展了多方面的基线评估，并对结果进行了总结。该数据集将在http://cvi2.uni.lu/spades上公开。
英文摘要：
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.
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。
