On-ground validation of a CNN-based monocular pose estimation system for uncooperative spacecraft: Bridging domain shift in rendezvous scenarios

Cassinis, Lorenzo Pasqualetto; Menicucci, A.; Gill, Eberhard; Ahrns, Ingo; Sánchez-Gestido, Manuel

doi:10.1016/j.actaastro.2022.04.002

Cited by 27 publications

(9 citation statements)

References 26 publications

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“…In particular, this latter approach struggles to be compliant with on-board resources due to database storage and its computationally heavy search. This problem has been tackled in recent years by applying Template Matching datasets to Artificial Intelligence approaches through the use of Convolutional Neural Networks [20,[24][25][26]. Alternatively, pose can be determined by taking the target geometrical characteristics into account during the design process [27].…”

Section: Literature Reviewmentioning

confidence: 99%

Relative Navigation Strategy About Unknown and Uncooperative Targets

Maestrini

Luca

Lizia

2023

Journal of Guidance, Control, and Dynamics

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In recent years, space debris has become a threat for satellites operating in low Earth orbit. Even by applying mitigation guidelines, their number will still increase over the course of the century. As a consequence, active debris removal missions and on-orbit servicing missions have gained momentum at both academic and industrial level. The crucial step in both scenarios is the capability of navigating in the neighborhood of a target resident space object. This problem has been tackled many times in literature with varying level of cooperativeness of the target required. While several techniques are available when the target is cooperative or its shape is known, no approach is mature enough to deal with uncooperative and unknown targets. This paper proposes a hybrid method to tackle this issue called Coarse Model-Based Relative Navigation (CoMBiNa). The main idea of this algorithm is to split the mission into two phases. During the first phase, the algorithm constructs a coarse model of the target. In the second phase, this coarse model is used as a reference for a relative navigation technique, effectively shifting the focus toward state and inertia estimation. In addition, this paper proposes a strategy to leverage the structure of the selected navigation method to detect and reject outliers. To conclude, CoMBiNa is tested on a simulated environment to highlight its benefits and its shortcomings, while also assessing its applicability on a limited-resource single-board computer.

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Section: Literature Reviewmentioning

confidence: 99%

Relative Navigation Strategy About Unknown and Uncooperative Targets

Maestrini

Luca

Lizia

2023

Journal of Guidance, Control, and Dynamics

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“…On-ground test facilities enable testing of these subsystems prior to launch. There are only a few research test beds for emulating motions in space, such as the European Proximity Operations Simulator (EPOS) at the German Aeronautics Centre (DLR) [12] consisting of two robot systems with 6-Dof and 7-Dof motion capabilities, the GNC Rendezvous and Landing Simulator (GRALS) at the European Space Research and Technology Centre (ES-TEC) in the Netherlands [13] that includes a 7-Dof robot and rail system with floating platforms, and the Testbed for Robotic Optical Navigation (TRON) in Stanford University [14] with two 7-Dof robot systems, among others. The Zero-G lab at the University of Luxembourg is a newly developed facility for performing such spatial motions.…”

Section: Need For On-ground Validationmentioning

confidence: 99%

On-Ground Validation of Orbital GNC: Visual Navigation Assessment in Robotic Testbed Facility

Muralidharan,

Makhdoomi,

Žinys

et al. 2023

Preprint

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CubeSats have become versatile platforms for various space missions (e.g. On-Orbit Servicing and debris removal), owing to their low cost and flexibility. Many of these tasks involve proximity operations, requiring precise guidance, navigation, and control (GNC) algorithms. Vision-based navigation is gaining traction for such operations, but extreme lighting conditions in space challenge optical techniques. This paper presents the ground validation of a GNC algorithm designed for autonomous satellite rendezvous, leveraging hardware-in-the-loop (HIL) experiments. The investigation focuses on two key areas. Firstly, the rationale behind the augmenting of the robot workspace (6-Dof UR10e robot + linear rail) to emulate relatively longer trajectories with complete position and orientation states. Secondly, the assessment of the control algorithm in response to uncertain pose observations from the vision-based navigation system. Results indicate increased control costs with uncertain navigation and exemplify the importance of on-ground testing for system validation prior to launch, especially in extreme cases that are typically difficult with software-based testing.

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“…The GNC Rendezvous, Approach and Landing Simulator (GRALS) testbed is situated in the Orbital Robotics and GNC Laboratory (ORGL) at the European Space Research and Technology Centre (ESTEC) [16]. A Prosilica GC2450 camera mounted on a KUKA robotic arm is used for capturing images at the facility.…”

Section: Grals Netherlandsmentioning

confidence: 99%

“…However, the construction of such facility entails a plethora of uncertainties as it is not a standardized nor well-documented process. Consequently, research centers undertaking this endeavour face many challenges, including a lack of support in the form of guides, manuals, or templates [10,15,16]. As any other development project, the major drawback from these uncertainties is the increased probability of cost overruns, project delays, and even project failure [17].…”

Section: Introductionmentioning

confidence: 99%

Lessons from a Space Lab: An Image Acquisition Perspective

Pauly,

Jamrozik,

del Castillo

et al. 2023

International Journal of Aerospace Engineering

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The use of deep learning (DL) algorithms has improved the performance of vision-based space applications in recent years. However, generating large amounts of annotated data for training these DL algorithms has proven challenging. While synthetically generated images can be used, the DL models trained on synthetic data are often susceptible to performance degradation when tested in real-world environments. In this context, the Interdisciplinary Center of Security, Reliability and Trust (SnT) at the University of Luxembourg has developed the “SnT Zero-G Lab,” for training and validating vision-based space algorithms in conditions emulating real-world space environments. An important aspect of the SnT Zero-G Lab development was the equipment selection. From the lessons learned during the lab development, this article presents a systematic approach combining market survey and experimental analyses for equipment selection. In particular, the article focuses on the image acquisition equipment in a space lab: background materials, cameras, and illumination lamps. The results from the experiment analyses show that the market survey complimented by experimental analyses is required for effective equipment selection in a space lab development project.

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On-ground validation of a CNN-based monocular pose estimation system for uncooperative spacecraft: Bridging domain shift in rendezvous scenarios

Cited by 27 publications

References 26 publications

Relative Navigation Strategy About Unknown and Uncooperative Targets

Relative Navigation Strategy About Unknown and Uncooperative Targets

On-Ground Validation of Orbital GNC: Visual Navigation Assessment in Robotic Testbed Facility

Lessons from a Space Lab: An Image Acquisition Perspective

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