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 hardware-in-the-loop 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+ is used in
the second international Satellite Pose Estimation Challenge co-hosted by SLAB
and the Advanced Concepts Team of the European Space Agency to evaluate and
compare the robustness of spaceborne ML models trained on synthetic images.

本文介绍了具有特定重点的下一代航天器姿态估计数据集，包括用于训练的 6 万个合成图像和由测试床拍摄的 9531 个具有准确和最大多样化姿态标签的硬件在环图像，旨在评估和比较基于合成图像训练的空间机器学习模型的鲁棒性。

SPEED+: 太空航天器姿态估计跨域差异的下一代数据集

SPEED+: Next-Generation Dataset for Spacecraft Pose Estimation across  Domain Gap

This work introduces the Spacecraft Pose Network (SPN) for on-board
estimation of the pose, i.e., the relative position and attitude, of a known
non-cooperative spacecraft using monocular vision. In contrast to other
state-of-the-art pose estimation approaches for spaceborne applications, the
SPN method does not require the formulation of hand-engineered features and
only requires a single grayscale image to determine the pose of the spacecraft
relative to the camera. The SPN method uses a Convolutional Neural Network
(CNN) with three branches to solve for the pose. The first branch of the CNN
bootstraps a state-of-the-art object detector to detect a 2D bounding box
around the target spacecraft. The region inside the bounding box is then used
by the other two branches of the CNN to determine the attitude by initially
classifying the input region into discrete coarse attitude labels before
regressing to a finer estimate. The SPN method then uses a novel Gauss-Newton
algorithm to estimate the position by using the constraints imposed by the
detected 2D bounding box and the estimated attitude. The secondary contribution
of this work is the generation of the Spacecraft PosE Estimation Dataset
(SPEED). SPEED consists of synthetic as well as actual camera images of a
mock-up of the Tango spacecraft from the PRISMA mission. The synthetic images
are created by fusing OpenGL-based renderings of the spacecraft's 3D model with
actual images of the Earth captured by the Himawari-8 meteorological satellite.
The actual camera images are created using a 7 degrees-of-freedom robotic arm,
which positions and orients a vision-based sensor with respect to a full-scale
mock-up of the Tango spacecraft. The SPN method, trained only on synthetic
images, produces degree-level attitude error and cm-level position errors when
evaluated on the actual camera images not used during training.

本文介绍了一种用于非合作航天器姿态估计的 Spacecraft Pose Network (SPN) 方法， 基于单目视觉，无需手工特征提取和仅使用一张灰度图像即可确定相机与航天器之间的姿态。SPN 方法使用一个卷积神经网络，在检测航天器的 2D 边界框的基础上，用三个分支来求解姿态和位置估计，并生成了 Spacecraft PosE Estimation Dataset (SPEED) 数据集，仅用合成图像训练的 SPN 方法能够在实际相机图像上产生角度级姿态误差和 cm 级位置误差。