利用事件传感弥合卫星位姿估计中的空间域差距
Towards Bridging the Space Domain Gap for Satellite Pose Estimation using Event Sensing
AI总结：
针对太空场景真实数据稀缺导致域适应困难的问题，提出仅用合成事件数据训练并结合基础增强的卫星位姿估计方法，依托实验室模拟交会数据集验证其可在剧烈光照下有效泛化。
摘要：
使用合成数据训练的深度模型需要进行域适应，以弥合仿真环境与目标环境之间的差距。最先进的域适应方法通常需要来自目标域的足量（未标注）数据。然而，当目标域是太空等极端环境时，这一需求很难得到满足。本文的目标问题是近距离卫星位姿估计，其中从实际交会任务中获取卫星图像的成本很高。我们证明，在光照差异显著的情况下，事件传感为从仿真到目标域的泛化提供了一种有前景的解决方案。我们的主要贡献是一种基于事件的卫星位姿估计技术，该技术仅在合成事件数据上训练，并采用基础数据增强来提高对实际（含噪）事件传感器的鲁棒性。支撑我们方法的是一个具有经过仔细标定的真值的新颖数据集，其中包含在实验室剧烈光照条件下模拟卫星交会场景所获得的真实事件数据。在该数据集上的结果表明，我们仅基于合成数据训练且未进行适应的基于事件的卫星位姿估计方法，能够有效泛化到目标域。
英文摘要：
Deep models trained using synthetic data require domain adaptation to bridge the gap between the simulation and target environments. State-of-the-art domain adaptation methods often demand sufficient amounts of (unlabelled) data from the target domain. However, this need is difficult to fulfil when the target domain is an extreme environment, such as space. In this paper, our target problem is close proximity satellite pose estimation, where it is costly to obtain images of satellites from actual rendezvous missions. We demonstrate that event sensing offers a promising solution to generalise from the simulation to the target domain under stark illumination differences. Our main contribution is an event-based satellite pose estimation technique, trained purely on synthetic event data with basic data augmentation to improve robustness against practical (noisy) event sensors. Underpinning our method is a novel dataset with carefully calibrated ground truth, comprising of real event data obtained by emulating satellite rendezvous scenarios in the lab under drastic lighting conditions. Results on the dataset showed that our event-based satellite pose estimation method, trained only on synthetic data without adaptation, could generalise to the target domain effectively.
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。
