Conference PaperPDF Available
Correction: CNN-Based Pose Estimation System for Close-
Proximity Operations Around Uncooperative Spacecraft
Author(s) Name: Lorenzo Pasqualetto Cassinis, Robert Fonod, Eberhard Gill, Ingo Arhns, Jesus Gil Fernandez
Author(s) Affiliations: Delft University of Technology, Airbus DS GmbH, ESTEC
Correction Notice
The second metric 𝐸𝑅 in table two should be in [deg], and not in [deg/s]
The term 𝒒 ⊗ 𝒓𝑖
𝐵𝒒 in Eqn. 17 and Eqn. 22 should be replaced with 𝒒 ⊗ 𝒓𝑖
Downloaded by on February 2, 2020 | | DOI: 10.2514/6.2020-1457.c1
AIAA Scitech 2020 Forum
6-10 January 2020, Orlando, FL 10.2514/6.2020-1457.c1
Copyright © 2020 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.
AIAA SciTech Forum
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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. This paper introduces a novel framework to enable robust monocular pose estimation for close-proximity operations around an uncooperative spacecraft, which combines a Convolutional Neural Network (CNN) for feature detection with a Covariant Efficient Procrustes Perspective-n-Points (CEPPnP) solver and a Multiplicative Extended Kalman Filter (MEKF). The performance of the proposed method is evaluated at different levels of the pose estimation system. A Single-stack Hourglass CNN is proposed for the feature detection step in order to decrease the computational load of the Image Processing (IP), and its accuracy is compared to the standard, more complex High-Resolution Net (HRNet). Subsequently, heatmaps-derived covariance matrices are included in the CEPPnP solver to assess the pose estimation accuracy prior to the navigation filter. This is done in order to support the performance evaluation of the proposed tightly-coupled approach against a loosely-coupled approach, in which the detected features are converted into pseudomeasurements of the relative pose prior to the filter. The performance results of the proposed system indicate that a tightly-coupled approach can guarantee an advantageous coupling between the rotational and translational states within the filter, whilst reflecting a representative measurements covariance. This suggest a promising scheme to cope with the challenging demand for robust navigation in close-proximity scenarios. 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 a representative close-proximity scenario for the validation of the proposed filter.
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