DOI: 10.5281/zenodo.6499007
title: Tango Spacecraft Dataset for Monocular Pose Estimation

description: <strong>Reference Paper:</strong> <strong>M. Bechini, M. Lavagna, P. Lunghi, Dataset generation and validation for spacecraft pose estimation via monocular images processing, Acta Astronautica 204 (2023) 358–369</strong> 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) <strong>General Description:</strong> The "<em>Tango Spacecraft Dataset for Monocular Pose Estimation</em>" 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. <strong>Images Information:</strong> 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. <br> <strong>Labels Information:</strong> 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. <strong>VERSION CONTROL</strong> 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 <em>"Tango Spacecraft Wireframe Dataset Model for Line Segments Detection"</em> v2.0 full-scale (DOI: https://doi.org/10.5281/zenodo.6372848) and also "<em>Tango Spacecraft Dataset for Region of Interest Estimation and Semantic Segmentation</em>" 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. <strong>These three datasets give the most comprehensive dataset of space borne synthetic images ever published</strong> (up to our knowledge).