Tango Spacecraft Dataset for Monocular Pose Estimation

Reference Paper: M. Bechini, M. Lavagna, P. Lunghi, Dataset generation and validation for spacecraft pose estimation via monocular images processing, Acta Astronautica 204 (2023) 358–369 M. Bechini, P. Lunghi, M. Lavagna. "Spacecraft Pose Estimation via Monocular Image Processing: Dataset Generation and Validation". In 9th European Conference for Aeronautics and Aerospace Sciences (EUCASS) General Description: The "Tango Spacecraft Dataset for Monocular Pose Estimation" dataset here published should be used for relative pose estimation tasks. It is split into 30002 train images and 3002 test images representing the Tango spacecraft from Prisma mission, being the largest publicly available dataset of synthetic space-borne noise-free images tailored to pose estimation tasks (up to our knowledge). The label of each image gives relative quaternion (in scalar-last format) between Tango and the camera (hence the relative position of the target with respect to the camera in camera reference frame) and the relative position of Tango with respect to the camera in camera reference frame. More information on the dataset split and on the label format are reported below. Images Information: The dataset comprises 30002 synthetic grayscale images of Tango spacecraft from Prisma mission that serves as train set, while the test set is formed by 3002 synthetic grayscale images of Tango spacecraft from Prisma mission in PNG format. About 1/6 of the images both in the train and in the test set have a non-black background, obtained by rendering an Earth-like model in the raytracing process used to define the images reported. The images are noise-free to increase the flexibility of the dataset. The illumination direction of the spacecraft in the scene is uniformly distributed in the 3D space in agreement with the Sun position constraints. The dataset contains also a .txt file with the parameters of the camera used to generate the images. Labels Information: Labels in the SPEED and SPEED+ dataset format are here provided in separated JSON files. The files are formatted per each image as in the following example: filename : tango_img_1 # name of the image to which the data are referred q_TRG2CAM : [qx qy qz qw] # relative quaternion from Target to Camera reference frame t_CAM2TRG : [x, y, z] # relative position of Tango with respect to the camera expressed in meters Notice that for making the usage of the dataset easier, both the training set and the test set are split in two folders containing the images with earth as background and without background. VERSION CONTROL v1.0: This version contains the dataset (both train and test) of full scale images with relative pose annotations. These images have width=height=1024 pixels. The position of tango with respect to the camera is randomly selected from a uniform distribution, but it is ensured the full visibility in all the images. Note: this dataset contains the same images of the "Tango Spacecraft Wireframe Dataset Model for Line Segments Detection" v2.0 full-scale (DOI: https://doi.org/10.5281/zenodo.6372848) and also "Tango Spacecraft Dataset for Region of Interest Estimation and Semantic Segmentation" v1.0 (DOI: https://doi.org/10.5281/zenodo.6507863) and they can be used together by combining the annotations of the relative pose and the ones of the reprojected wireframe model of Tango, with also the ones of the ROI. These three datasets give the most comprehensive dataset of space borne synthetic images ever published (up to our knowledge).

参考文献：M. Bechini、M. Lavagna、P. Lunghi发表于《Acta Astronautica》204卷（2023年）第358–369页的论文《基于单目图像处理的航天器位姿估计数据集生成与验证（Dataset generation and validation for spacecraft pose estimation via monocular images processing）》，以及M. Bechini、P. Lunghi、M. Lavagna收录于第9届欧洲航空航天科学大会（EUCASS）的论文《基于单目图像处理的航天器位姿估计：数据集生成与验证（Spacecraft Pose Estimation via Monocular Image Processing: Dataset Generation and Validation）》

通用说明：本次发布的「Tango航天器单目位姿估计数据集（Tango Spacecraft Dataset for Monocular Pose Estimation）」适用于相对位姿估计任务。数据集划分为30002张训练图像与3002张测试图像，所有图像均对应普里斯玛（Prisma）任务中的Tango航天器。据我们所知，这是目前公开可用的、专为位姿估计任务打造的规模最大的合成空间无噪图像数据集。每张图像的标签包含：Tango航天器与相机之间的相对四元数（scalar-last格式，即标量后置格式），以及相机参考系下Tango航天器相对于相机的相对位置。关于数据集划分与标签格式的更多细节如下文所述。

图像信息：训练集包含30002张合成灰度图像，均为普里斯玛任务中的Tango航天器；测试集则由3002张PNG格式的合成灰度Tango航天器图像组成。训练集与测试集中各有约1/6的图像带有非黑色背景，该背景通过在渲染图像的光线追踪过程中添加类地模型生成。为提升数据集的灵活性，所有图像均无噪声。场景中航天器的光照方向在三维空间中均匀分布，符合太阳位置约束条件。此外，数据集还包含一个.txt文件，记录了用于生成图像的相机参数。

标签信息：本次数据集采用SPEED与SPEED+数据集的标签格式，存储于独立的"JSON"文件中。每个图像对应的标签格式示例如下：
filename : tango_img_1 # 对应图像的文件名
q_TRG2CAM : [qx qy qz qw] # 从目标（Tango航天器）到相机参考系的相对四元数
t_CAM2TRG : [x, y, z] # 以米为单位的、相机参考系下Tango航天器相对于相机的相对位置
为方便数据集使用，训练集与测试集均被划分为两个子文件夹，分别包含带有地球背景与无背景的图像。

版本控制 v1.0：本版本包含全尺寸的训练集与测试集数据集，附带相对位姿标注。所有图像的宽高均为1024像素。Tango航天器相对于相机的位置从均匀分布中随机选取，且确保所有图像中目标均完全可见。
注：本数据集与v2.0版本的「Tango航天器线段检测线框模型数据集（Tango Spacecraft Wireframe Dataset Model for Line Segments Detection）」（DOI: https://doi.org/10.5281/zenodo.6372848）以及v1.0版本的「Tango航天器感兴趣区域估计与语义分割数据集（Tango Spacecraft Dataset for Region of Interest Estimation and Semantic Segmentation）」（DOI: https://doi.org/10.5281/zenodo.6507863）包含相同的图像。可通过结合相对位姿标注、Tango航天器重投影线框模型标注以及感兴趣区域（Region of Interest，ROI）标注，将这三个数据集合并使用。据我们所知，这三个数据集共同构成了迄今为止发布的最全面的空间合成图像数据集。

搜集汇总

背景与挑战

背景概述

该数据集是一个用于单目姿态估计的合成图像数据集，包含33004张Tango航天器的灰度图像（训练集30002张，测试集3002张），图像尺寸为1024x1024像素，部分带有地球背景。数据集提供详细的姿态和位置标签（以JSON格式），专为计算机视觉中的相对姿态估计任务设计，是目前最大的公开合成空间图像数据集之一。