Optical navigation for Lunar landing based on Convolutional Neural Network crater detector

Silvestrini, Stefano; Piccinin, Margherita; Zanotti, Giovanni; Brandonisio, Andrea; Bloise, Ilaria; Feruglio, Lorenzo; Lunghi, Paolo; Lavagna, Michèle; Varile, Mattia

doi:10.1016/j.ast.2022.107503

Cited by 26 publications

(3 citation statements)

References 44 publications

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“…The work of Christian provides a linear least-square estimate for the general crater positioning estimation problem and will be used here [47]. Traditional crater identification algorithms have been studied [50], but machine learning and Artificial Intelligence (AI) present higher performances, popularly adopted by [16,47,51].…”

Section: Crater Navigationmentioning

confidence: 99%

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Autonomous and Earth-Independent Orbit Determination for a Lunar Navigation Satellite System

Critchley-Marrows,

Wu,

Kawabata

et al. 2024

Aerospace

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In recent years, the number of expected missions to the Moon has increased significantly. With limited terrestrial-based infrastructure to support this number of missions, as well as restricted visibility over intended mission areas, there is a need for space navigation system autonomy. Autonomous on-board navigation systems in the lunar environment have been the subject of study by a number of authors. Suggested systems include optical navigation, high-sensitivity Global Navigation Satellite System (GNSS) receivers, and navigation-linked formation flying. This paper studies the interoperable nature and fusion of proposed autonomous navigation systems that are independent of Earth infrastructure, given challenges in distance and visibility. This capability is critically important for safe and resilient mission architectures. The proposed elliptical frozen orbits of lunar navigation satellite systems will be of special interest, investigating the derivation of orbit determination by non-terrestrial sources utilizing celestial observations and inter-satellite links. Potential orbit determination performances around 100 m are demonstrated, highlighting the potential of the approach for future lunar navigation infrastructure.

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Section: Crater Navigationmentioning

confidence: 99%

“…The ENU frame is highlighted on the projected equator in Figure 7. The ellipse relation between A i and C i , after appropriate identification by a machine learning algorithm [16,51], can be related through a series of projection transformations for the spacecraft located at a position r P [47].…”

Section: Crater Navigationmentioning

confidence: 99%

Autonomous and Earth-Independent Orbit Determination for a Lunar Navigation Satellite System

Critchley-Marrows,

Wu,

Kawabata

et al. 2024

Aerospace

View full text Add to dashboard Cite

show abstract

“…Convolutional neural networks (CNNs) have been extensively used in studies to automatically detect and describe craters for use as landmarks in navigation [11]- [14]. However, recent visits to small solar system bodies suggest that sub-kilometer objects do not possess many suitable craters for this purpose.…”

Section: Related Work a Features For Navigation Near An Asteroidmentioning

confidence: 99%

CNN-Based Local Features for Navigation Near an Asteroid

Knuuttila,

Kestilä,

Kallio

2024

IEEE Access

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This article addresses the challenge of vision-based proximity navigation in asteroid exploration missions and on-orbit servicing. Traditional feature extraction methods struggle with the significant appearance variations of asteroids due to limited scattered light. To overcome this, we propose a lightweight feature extractor specifically tailored for asteroid proximity navigation, designed to be robust to illumination changes and affine transformations. We compare and evaluate state-of-the-art feature extraction networks and three lightweight network architectures in the asteroid context. Our proposed feature extractors and their evaluation leverage synthetic images and real-world data from missions such as NEAR Shoemaker, Hayabusa, Rosetta, and OSIRIS-REx. Our contributions include a trained feature extractor, incremental improvements over existing methods, and a pipeline for training domain-specific feature extractors. Experimental results demonstrate the effectiveness of our approach in achieving accurate navigation and localization. This work aims to advance the field of asteroid navigation and provides insights for future research in this domain.

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Imbalanced Data Handling for Deep Learning-Based Autonomous Crater Detection Algorithms in Terrain Relative Navigation

Latorre

Spiller

Curti

2023

Studies in Computational Intelligence

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Optical navigation for Lunar landing based on Convolutional Neural Network crater detector

Cited by 26 publications

References 44 publications

Autonomous and Earth-Independent Orbit Determination for a Lunar Navigation Satellite System

Autonomous and Earth-Independent Orbit Determination for a Lunar Navigation Satellite System

CNN-Based Local Features for Navigation Near an Asteroid

Imbalanced Data Handling for Deep Learning-Based Autonomous Crater Detection Algorithms in Terrain Relative Navigation

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