CNN-Based Pose Estimation System for Close-Proximity Operations Around Uncooperative Spacecraft

CNN-Based Pose Estimation System for Close-Proximity Operations Around Uncooperative Spacecraft

Pasqualetto Cassinis, L. (TU Delft Space Systems Egineering)
Fonod, R. (TU Delft Space Systems Egineering)
Gill, E.K.A. (TU Delft Space Engineering; TU Delft Space Systems Egineering)
Ahrns, Ingo (Airbus Defence and Space)
Gil Fernandez, Jesus (European Space Agency (ESA))

Space Engineering

2020

This paper introduces a novel framework which combines a Convolutional Neural Network (CNN) for feature detection with a Covariant Efficient Procrustes Perspective-n-Points (CEPPnP) solver and an Extended Kalman Filter (EKF) to enable robust monocular pose estimation for close-proximity operations around an uncooperative spacecraft. The relative pose estimation of an inactive spacecraft by an active servicer spacecraft is a critical task in the design of current and planned space missions, due to its relevance for close-proximity operations, such as In-Orbit Servicing and Active Debris Removal. The main contribution of this work stands in deriving statistical information from the Image Processing step, by associating a covariance matrix to the heatmaps returned by the CNN for each detected feature. This information is included in the CEPPnP to improve the accuracy of the pose estimation step during filter initialization. The derived measurement covariance matrix is used in a tightly-coupled EKF to allow an enhanced representation of the measurements error from the feature detection step. This increases the filter robustness in case of inaccurate CNN detections. The proposed method is capable of returning reliable estimates of the relative pose as well as of the relative translational and rotational velocities, under adverse illumination conditions and partial occultation of the target. Synthetic 2D images of the European Space Agency's Envisat spacecraft are used to generate datasets for training, validation and testing of the CNN. Likewise, the images are used to recreate representative close-proximity scenarios for the validation of the proposed method.

http://resolver.tudelft.nl/uuid:71936e70-e3bd-400a-926b-11ae27795acd

https://doi.org/10.2514/6.2020-1457

American Institute of Aeronautics and Astronautics Inc. (AIAA)

9781624105951

AIAA Scitech 2020 Forum: 6-10 January 2020, Orlando, FL

AIAA Scitech 2020 Forum, 2020-01-06 → 2020-01-10, Orlando, United States

AIAA Scitech 2020 Forum, 1 PartF

Institutional Repository

conference paper

© 2020 L. Pasqualetto Cassinis, R. Fonod, E.K.A. Gill, Ingo Ahrns, Jesus Gil Fernandez