An Image Processing Pipeline for Autonomous Deep-Space Optical Navigation

A new era of space exploration and exploitation is fast approaching. A multitude of spacecraft will flow in the future decades under the propulsive momentum of the new space economy. Yet, the flourishing proliferation of deep-space assets will make it unsustainable to pilot them from ground with standard radiometric tracking. The adoption of autonomous navigation alternatives is crucial to overcoming these limitations. Among these, optical navigation is an affordable and fully ground-independent approach. Probes can triangulate their position by observing visible beacons, e.g., planets or asteroids, by acquiring their line-of-sight in deep space. To do so, developing efficient and robust image processing algorithms providing information to navigation filters is a necessary action. This paper proposes an innovative pipeline for unresolved beacon recognition and line-of-sight extraction from images for autonomous interplanetary navigation. The developed algorithm exploits the k-vector method for the non-stellar object identification and statistical likelihood to detect whether any beacon projection is visible in the image. Statistical results show that the accuracy in detecting the planet position projection is independent of the spacecraft position uncertainty. Whereas, the planet detection success rate is higher than 95% when the spacecraft position is known with a 3sigma accuracy up to 10^5 km.Comment: 26 pages, 7 figure

Analytical shape uncertainties in the polyhedron gravity model

The exploration of small bodies in the Solar system and the ability to perform remote or in situ science is tied to the understanding of the dynamical environment of such objects. As such, the evaluation of the gravity field arising from small bodies is key to this understanding. However, remote observations can only produce shape estimates, from which only uncertain gravity fields can be computed. The current disconnect in the literature between the uncertainty in the shape and that of the gravity field properties is detrimental to small body science and robust mission design. In particular, the literature does not provide any quantitative means to capture this link in the polyhedron gravity model, one of the main gravity model representations. With this in mind, this paper derives the expressions of the first variations and partial derivatives in the potential, acceleration and slopes computed from the polyhedron gravity model with respect to the vertices of the underlying body. These formulae are then combined with a Gaussian description of the uncertainty in the vertex coordinates so as to obtain analytical predictions of the potential, slope variances as well as the covariance in the acceleration at arbitrary locations around the body, treated as a stochastic shape. This linearized analytical approach was able to capture the statistical variation in the dynamical environment about asteroid 25143 Itokawa and 16 Psyche under the assumption of stochastic errors in the bodies’ shape models, at a lower computational cost than Monte–Carlo simulations. These methods should be of benefit to planetary scientists and mission designers seeking for more insight into the dynamical environment of uncertain small body shapes

Novel TRIM32 mutation in sarcotubular myopathy

Tripartite motif-containing protein 32 (TRIM32) is a member of the TRIM ubiquitin E3 ligases which ubiquitinates different substrates in muscle including sarcomeric proteins. Mutations in TRIM32 are associated with Limb-Girdle Muscular Dystrophy 2H. In a 66 old woman with disto-proximal myopathy, we identified a novel homozygous mutation of TRIM32 gene c.1781G > A (p. Ser594Asn) localised in the c-terminus NHL domain. Mutations of this domain have been also associated to Sarcotubular Myopathy (STM), a form of distal myopathy with peculiar features in muscle biopsy, now considered in the spectrum of LGMD2H. Muscle biopsy revealed severe abnormalities of the myofibrillar network with core like areas, lobulated fibres, whorled fibres and multiple vacuoles. Desmin and Myotilin stainings also pointed to accumulation as in Myofibrillar Myopathy. This report further confirms that STM and LGMD2H represent the same disorder and suggests to consider TRIM32 mutations in the genetic diagnosis of Sarcotubular Myopathy and Myofibrillar Myopathy

The ETNA mission concept: Assessing the habitability of an active ocean world

Enceladus is an icy world with potentially habitable conditions, as suggested by the coincident presence of a subsurface ocean, an active energy source due to water-rock interactions, and the basic chemical ingredients necessary for terrestrial life. Among all ocean worlds in our Solar System, Enceladus is the only active body that provides direct access to its ocean through the ongoing expulsion of subsurface material from erupting plumes. Here we present the Enceladus Touchdown aNalyzing Astrobiology (ETNA) mission, a concept designed during the 2019 Caltech Space Challenge. ETNA’s goals are to determine whether Enceladus provides habitable conditions and what (pre-) biotic signatures characterize Enceladus. ETNA would sample and analyze expelled plume materials at the South Polar Terrain (SPT) during plume fly-throughs and landed operations. An orbiter includes an ultraviolet imaging spectrometer, an optical camera, and radio science and a landed laboratory includes an ion microscope and mass spectrometer suite, temperature sensors, and an optical camera, plus three seismic geophones deployed during landing. The nominal mission timeline is 2 years in the Saturnian system and ∼1 year in Enceladus orbit with landed operations. The detailed exploration of Enceladus’ plumes and SPT would achieve broad and transformational Solar System science related to the building of habitable worlds and the presence of life elsewhere. The nature of such a mission is particularly timely and relevant given the recently released Origins, Worlds, and Life: A Decadal Strategy for Planetary Science and Astrobiology 2023–2032, which includes a priority recommendation for the dedicated exploration of Enceladus and its habitable potential

Calathus: A sample-return mission to Ceres

Ceres, as revealed by NASA's Dawn spacecraft, is an ancient, crater-saturated body dominated by low-albedo clays. Yet, localised sites display a bright, carbonate mineralogy that may be as young as 2 Myr. The largest of these bright regions (faculae) are found in the 92 km Occator Crater, and would have formed by the eruption of alkaline brines from a subsurface reservoir of fluids. The internal structure and surface chemistry suggest that Ceres is an extant host for a number of the known prerequisites for terrestrial biota, and as such, represents an accessible insight into a potentially habitable “ocean world”. In this paper, the case and the means for a return mission to Ceres are outlined, presenting the Calathus mission to return to Earth a sample of the Occator Crater faculae for high-precision laboratory analyses. Calathus consists of an orbiter and a lander with an ascent module: the orbiter is equipped with a high-resolution camera, a thermal imager, and a radar; the lander contains a sampling arm, a camera, and an on-board gas chromatograph mass spectrometer; and the ascent module contains vessels for four cerean samples, collectively amounting to a maximum 40 g. Upon return to Earth, the samples would be characterised via high-precision analyses to understand the salt and organic composition of the Occator faculae, and from there to assess both the habitability and the evolution of a relict ocean world from the dawn of the Solar System.The attached document is the authors’ final accepted version of the journal article provided here with a Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) Creative Commons Licence. You are advised to consult the publisher’s version if you wish to cite from it.

Navigation basée vision autonome et reconstruction de forme d’un astéroïde inconnu pendant la phase d’approche

Le but de cette thèse est de présenter des algorithmes basés vision pour permettre la navigation autonome de la sonde et la caractérisation d'un astéroïde inconnu avec une camera monoculaire pendant la phase d'approche.L'état de l'art de la navigation des sondes interplanétaires est basé sur un précis suivi radiométrique et des mesures optiques utilisés pour reconstruire la trajectoire relative par rapport à l'astéroïde. Dans le future proche le système de suivi radiométrique, le Deep Space Network (DSN), sera fortement surchargé puisque de plus en plus de missions sont conçues et seront opérées pour explorer le Système Solaire interne. La navigation autonome est une des solution envisagées afin de réduire la charge du DSN puisque ces algorithmes de navigation permettront à la sonde de se localiser pendant toutes les phases de la mission sans le support du sol.En outre, les caractéristiques des astéroïdes ne sont pas très bien connues avant l'arrivée car l'estimation avec observation à distance nécessite des hypothèses sur les propriétés du petit corps, comme l'albedo et la densité. Par conséquent il est nécessaire d'estimer pendant la mission les propriétés de l'astéroïde pour permettre une localisation précise et un retour scientifique maximale.La navigation autonome pourrait se mettre en place avec plusieurs senseurs mais les cameras sont normalement choisies car elles sont plus légères, plus compactes et nécessitent moins de puissance si comparées avec des autres senseurs, comme les LiDARs. Ce choix implique que les budgets de masse et de puissance du satellite ne sont pas fortement affectés pendant la phase de conception. Pour ces raisons, l'utilisation des cameras en combinaison avec des algorithmes de traitement d'image assure des bonnes performances de navigation avec des composants légers et rentables.Pendant l'approche à un petit corps la reconstruction de la forme et l'estimation de l'axe de rotation sont des étapes vitales. D'un côté, la forme permet d'avoir une première estimation du champ gravitationnel, avec l'hypothèse de densité constante. En plus, l'estimation de la forme permet d'utiliser les algorithme de navigation basée modèle. De l'autre côté, la connaissance de l'axe de rotation est centrale pour définir les repères de mission et pour estimer la localisation relative par rapport à l'astéroïde.La recherche poursuivie pendant ce doctorat a été divisée en trois sections:1. Premièrement, un algorithme d'estimation de forme basé silhouettes a été développé avec l'hypothèse de connaitre la localisation relative2. Deuxièmement, an algorithme pour reconstruire la forme de l'astéroïde et son axe de rotation a été conçu avec l'hypothèse de connaitre le facture d'échelle et d'avoir une estimation de la trajectoire inertielle3. Enfin, la quantification des incertitudes sur les harmoniques sphériques générées par une reconstruction de forme stochastique a été mise en place avec l'hypothèse d'avoir une densité constante et connue.The aim of this thesis is to present a vision-based solution to enable autonomous navigation and characterization of an unknown asteroid using a monocular camera during approach.Current approach to asteroid navigation relies on precise radiometric tracking and optical data to reconstruct the relative trajectory with respect to the asteroid. In the next future the radiometric tracking system, the Deep Space Network (DSN), will be overwhelmed as more missions are designed and will be launched to explore the inner Solar System. Autonomous navigation is an envisaged solution to reduce the DSN load as autonomous algorithms would enable the spacecraft to localize itself during all mission phases without ground support.Furthermore, asteroids characteristics are poorly known before arrival as their Earth-based estimation relies on hypothesis on their properties, such as albedo and density. It is thus required to estimate during the mission the asteroid properties which allow precise localization and compelling science.Autonomous navigation can be performed with several sensors but passive cameras are preferred as they are light, compact and low power demanding if compared with other sensors, such as LiDARs. This implies that cameras do not affect the mass and power budgets during the spacecraft design. For these reasons the use of passive cameras in combination with image processing algorithm provides competitive navigation performances with light and cost-effective hardware.During close approach to a small body it is of crucial interest to reconstruct the small body shape and to estimate the small body rotation axis. The former is an important milestone to investigate the gravity field under the assumption of constant density and to allow the use of model-based navigation algorithms. The latter is important to define reference frames and to estimate the small body relative localization.The research of this PhD thesis has been divided in three main sections:1. Firstly, a shape from silhouette algorithm has been developed under the assumption of knowing the relative localization to perform shape reconstruction using limbs information.2. Secondly, an algorithm to both reconstruct the asteroid shape and estimate the rotation axis from images has been developed under the assumption of knowing the scale factor and of having an estimate of the inertial trajectory.3. Finally, the uncertainty quantification on the spherical harmonics arising from a stochastic shape reconstruction has been characterized under the assumption of known constant density

The TinyV3RSE Hardware-in-the-Loop Vision-Based Navigation Facility

The increase in number of interplanetary probes has emphasized the need for spacecraft autonomy to reduce overall mission costs and to enable riskier operations without ground support. The perception of the external environment is a critical task for autonomous probes, being fundamental for both motion planning and actuation. Perception is often achieved using navigation sensors which provide measurements of the external environment. For space exploration purposes, cameras are among the sensors that provide navigation information with few constraints at the spacecraft system level. Image processing and vision-based navigation algorithms are exploited to extract information about the external environment and the probe’s position within it from images. It is thus crucial to have the capability to generate realistic image datasets to design, validate, and test autonomous algorithms. This goal is achieved with high-fidelity rendering engines and with hardware-in-the-loop simulations. This work focuses on the latter by presenting a facility developed and used at the Deep-space Astrodynamics Research and Technology (DART) Laboratory at Politecnico di Milano. First, the facility design relationships are established to select hardware components. The critical design parameters of the camera, lens system, and screen are identified and analytical relationships are developed among these parameters. Second, the performances achievable with the chosen components are analytically and numerically studied in terms of geometrical accuracy and optical distortions. Third, the calibration procedures compensating for hardware misalignment and errors are defined. Their performances are evaluated in a laboratory experiment to display the calibration quality. Finally, the facility applicability is demonstrated by testing imageprocessing algorithms for space exploration scenarios