Spacecraft Pose Estimation via Monocular Image Processing: Dataset Generation and Validation
Creators: name:  M. BECHINI 
name:   P. LUNGHI 
name:   M. LAVAGNA 
Publisher: name: Proceedings of  the 9th European Conference for Aerospace Sciences. Lille, France, 27 June - 1 July, 2022
Year: 2022
Subjects: FLIGHT DYNAMICS
GNC
AVIONICS
EQUIPMENT
IMAGE PROCESSING

description: New classes of missions that envision a major role for autonomous close proximity operations, like Formation Flying and On-Orbit Servicing, Active Debris Removal included, gained increasing attention in the last years, with particular interest on non-cooperative objects [1]. The chaser reactivity, those scenarios need, impose a relevant level of autonomy to timely determine on board its pose (position and attitude) relative to the target and properly react; among others, sensor suite which include a monocular camera is largely considered one of the most attractive solution to acquire measurements for the on board GNC chain [2]. State of the art Artifical Neural Networks (ANN) revealed, in many different contexts, to be a valuable tool to then process acquired images and feed the pose estimation final block in the navigation chain [3]. Even very powerful, ANNs need valid training set to build a solid correlation among the variables in their settling phase. Despite that training sets are the crucial node in the ANNs based attainable algorithms robustness and effectiveness, the authors detected a lack of publicly available datasets for training and testing the vision-based algorithms [3]. In fact, only the Spacecraft Pose Estimation Dataset (SPEED) [4] is publicly available up to now. The current work presents the in-house realisation of a validated dataset of synthetic noise-free images to adopt during the training and testing campaigns of ANN for image processing in the field of vision based relative and autonomous navigation. The proposed dataset includes 30000 synthetic images, being the largest available dataset of space-related images (double the size of the SPEED dataset); to preserve flexibility, images are provided noiseless: noise can be then added in post-processing; POV-Ray as ray-tracer is adopted for images generation. The target spacecraft adopted is a simplified version of the Tango satellite from PRISMA mission (as in [4]); the 5000 images background, represented by the Earth, has been fully modeled in POV-Ray as an ellipsoid with terrain and water optical properties differentiated to increase the fidelity; more, the Earth surface roughness due to mountains and valleys as well as a random cloud layer rotation and a model of the atmosphere with related scattering properties have been included in each image, to provide higher fidelity with respect to the available spaceborne pictures of the Earth. A 10005000 km altitude band has been selected to generate the images sets, taken around the Earth with uniform distribution. Each generated image in the dataset is labeled with target and camera relative and absolute pose, and sun position. The paper shows the performed analysis to ensure an acceptable level of randomness in the produced dataset, as reported in [3]. The paper also discusses results output by the images qualitative and quantitative validation campaign performed to assess the the ray-tracer fidelity and the simplified Tango model adopted with respect to the SPEED data-set, here assumed as ground truth. The analysis highlights that the relative poses represented in the images are well distributed and representative of a wide range of scenarios. The output of the validation campaign shows that the images obtained via POV-Ray are comparable to the SPEED dataset and so are representative of real case scenarios.