Autonomous vision-based spaceborne navigation is an enabling technology for future on-orbit servicing and space logistics missions. While computer vision in general has benefited from Machine Learning (ML), training and validating spaceborne ML models are extremely challenging due to the impracticality of acquiring a large-scale labeled dataset of images of the intended target in the space environment. Existing datasets, such as Spacecraft PosE Estimation Dataset (SPEED), have so far mostly relied on synthetic images for both training and validation, which are easy to mass-produce but fail to resemble the visual features and illumination variability inherent to the target spaceborne images. In order to bridge the gap between the current practices and the intended applications in future space missions, this paper introduces SPEED+: the next generation spacecraft pose estimation dataset with specific emphasis on domain gap. In addition to 60,000 synthetic images for training, SPEED+ includes 9,531 simulated images of a spacecraft mockup model captured from the Testbed for Rendezvous and Optical Navigation (TRON) facility. TRON is a first-of-a-kind robotic testbed capable of capturing an arbitrary number of target images with accurate and maximally diverse pose labels and high-fidelity spaceborne illumination conditions. SPEED+ will be used in the upcoming international Satellite Pose Estimation Challenge co-hosted with the Advanced Concepts Team of the European Space Agency to evaluate and compare the robustness of spaceborne ML models trained on synthetic images.
翻译:自主的视觉空间导航是未来在轨服务和空间物流飞行任务的一种扶持性技术,虽然计算机视觉总体上受益于机器学习(ML),但培训和验证空间ML模型由于获取空间环境预期目标图像的大规模标记数据集不切实际,因此由于获取空间环境预期目标图像的大规模标签数据集不切实际,因此,培训和验证空间ML模型极具挑战性;现有数据集,如航天器PosEE动动动动动数据数据集(SPEEEED),迄今为止主要依赖合成图像进行培训和验证,这些图像易于大规模生成,但不能与目标空间传播图像所固有的视觉特征和照明变异性相类似;为了缩小当前做法与未来空间飞行任务中预期应用之间的差距,本文件介绍了SPEEED+:下一代航天器构成估算数据集,特别强调领域差距;除了60,SPEEED+(SPEEED)+(SPEEEED)数据库,包括9 531个航天器模拟模型模拟图像,这些模拟模型从Redivous和光学实验室(TRON)测试台)设施采集的航天器模拟模型,但并不象样的图像和高端图像的欧洲高级机器人测试模型将首次用于高端的SBEPEBEBIBIBIBA的高级图像。