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

Lorenzo Pasqualetto Cassinis, R. Fonod, Eberhard Gill, Ingo Ahrns, Jesus Gil Fernandez

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

7 Citations (Scopus)
329 Downloads (Pure)


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.
Original languageEnglish
Title of host publicationAIAA Scitech 2020 Forum
Subtitle of host publication6-10 January 2020, Orlando, FL
PublisherAmerican Institute of Aeronautics and Astronautics Inc. (AIAA)
Number of pages16
ISBN (Electronic)978-1-62410-595-1
ISBN (Print)9781624105951
Publication statusPublished - 2020
EventAIAA Scitech 2020 Forum - Orlando, United States
Duration: 6 Jan 202010 Jan 2020

Publication series

NameAIAA Scitech 2020 Forum
Volume1 PartF


ConferenceAIAA Scitech 2020 Forum
Country/TerritoryUnited States


Dive into the research topics of 'CNN-Based Pose Estimation System for Close-Proximity Operations Around Uncooperative Spacecraft'. Together they form a unique fingerprint.

Cite this