During space missions, real time tele-operation of a rover is not practical because of significant signal latencies associated with inter planetary distances, making some degree of autonomy in rover control desirable. One of the challenges to achieving autonomy is the determination of terrain traversability. As part of this field, the determination of motion state of a rover on rough terrain via the estimation of wheel-terrain contact angles is proposed. This thesis investigates the feasibility of estimating the contact angles from the kinematics of the rover system and measurements from the onboard inertial measurement unit (IMU), joint angle sensors and wheel encoders. This approach does not rely on any knowledge of the terrain geometry or terrain mechanical properties. An existing framework of rover velocity and wheel slip estimation for flat terrain has been extended to additionally estimate the wheel-terrain contact angle along with a side slip angle for each individual wheel, for rough terrain drive. A random walk and a damped model are used to describe the evolution of the contact angle and side slip angle over an unknown terrain. A standard strapdown algorithm for the estimation of attitude and velocity from IMU measurements, is modified to incorporate the 3D kinematics of the rover in the implementation of a nonlinear Kalman filter to estimate the motion states. The estimation results from the filter are verified using tests performed on the ExoMars BB2. The obtained contact angle estimates are found to be consistent with the reference values.

Dans le cadre du programme ExoMars, l’ESA va déployer un rover sur Mars dont la mission sera de réaliser des prélèvements d’échantillons par forage souterrain et les analyser à l’aide des instruments scientifiques embarqués. Pour atteindre en toute sécurité les différents points d’intérêt où seront effectués ces prélèvements, le rover devra être capable de parcourir plus de 70 mètres par sol (jour martien) tout en respectant les limitations des communications interplanétaires. Les performances des algorithmes de navigation autonome embarqués impacteront directement la réussite scientifique de cette mission. Le premier objectif de cette thèse est d’améliorer les performances de l’architecture de planification de chemin local itératif proposée par le CNES. Tout d’abord, l’utilisation d’un planificateur incrémental de chemin local ”Fringe Retrieving A∗” permettant de réduire la charge de calcul est proposée. Il est complété par l’introduction de tas binaires dans les structures de gestion de la liste de priorité du planificateur de chemin.Ensuite, les manœuvres de rotation sur place pendant l’exécution des trajectoires sont réduites à l’aide d’un planificateur de chemins non-holonomes. Ce planificateur utilise un ensemble de chemins pré-calculés en tenant compte des capacités de braquage du rover. Le second axe de recherche concerne la planification de chemin global d’un rover d’exploration planétaire. Dans un premier temps, la contrainte de mémoire embarquée est détendue et une étude statistique évalue la pertinence d’un planificateur de chemin de type D∗ lite. Dans un deuxième temps, une nouvelle représentation multi-résolution de la carte de navigation est proposée pour stocker de plus grandes zones explorées par le rover sans augmenter l’utilisation de la mémoire embarquée. Cette représentation est utilisée par la suite par un planificateur de chemin global qui réduit automatiquement la charge de calcul en adaptant le sens de recherche en fonction de la forme et de la distribution des obstacles dans l’espace de navigation. / ESA’s ExoMars mission will deploy a 300kg class rover on Mars, which will serveas a mobile platform for the onboard scientific instruments to reach safely desired locations where subsurface drilling and scientific measurements are scheduled. Due to the limited inter-planetary communication constraints, full autonomous on board navigation capabilities are crucial as the rover has to drive over 70 meters per sol(Martian day) to reach designated scientific sites. The core of the navigation softwareto be deployed on the ExoMars rover uses as baseline the autonomous navigation architecture developed by CNES during the last 20 years. Such algorithms are designed to meet the mission-specific constraints imposed by the available spatial technology such as energy consumption, memory, computation power and time costs.The first objective of this thesis is to improve the performance of the successive localpath planning architecture proposed by CNES. First, the use of an increment allocal path planner, Fringe Retrieving A∗, is proposed to reduce the path planning computation load. This is complemented by the introduction of binary heaps in the management structures of the path planner. In-place-turn maneuvers during trajectory execution are further reduced by using a state lattice path planner which encodes the steering capabilities of the rover.The second research direction concerns global path planning capabilities for roboticplanetary exploration. First the onboard memory constraints are relaxed and a studyevaluating the use of a global D∗ lite path planner is performed. Second, a novel multi-resolution representation of the navigation map which covers larger areas atno memory cost increase is proposed. It is further used by a global path planner which automatically reduces the computational load by selecting its search direction based on obstacle shapes and distribution in the navigation space.

No / A profile across 8 layers from a fossil travertine terrace from a low temperature geothermal spring located in Svalbard, Norway has been studied using both Raman spectroscopy and SEM (Scanning Electron Microscopy) techniques to identify minerals and organic life signals. Calcite, anatase, quartz, haematite, magnetite and graphite as well as scytonemin, three different carotenoids, chlorophyll and a chlorophyll-like compound were identified as geo- and biosignatures respectively, using 785 and/or 514 nm Raman laser excitation wavelengths. No morphological biosignatures representing remnant microbial signals were detected by high-resolution imaging, although spectral analyses indicated the presence of organics. In contrast, in all layers, Raman spectra identified a series of different organic pigments indicating little to no degradation or change of the organic signatures and thus indicating the preservation of fossil biomarker compounds throughout the life time of the springs despite the lack of remnant morphological indicators. With a view towards planetary exploration we discuss the implications of the differences in Raman band intensities observed when spectra were collected with the different laser excitations. We show that these differences, as well as the different detection capability of the 785 and 514 nm laser, could lead to ambiguous compound identification. We show that the identification of bio and geosignatures, as well as fossil organic pigments, using Raman spectroscopy is possible. These results are relevant since both lasers have been considered for miniaturized Raman spectrometers for planetary exploration.

The purpose of this thesis work was to develop a Hardware-in-the-Loop imaging setup that enables experimenting with an event-based and frame-based camera under simulated space conditions. The generated data sets were used to compare visual navigation algorithms in terms of an event-based and frame-based feature detection and tracking algorithm. The comparative analyses of the feature detection and tracking algorithms were used to get insights into the feasibility of event-based vision near-space space objects. Event-based cameras differ from frame-based cameras by how they produce an asynchronous and independent stream of events caused by brightness changes at each pixel instead of capturing images at a fixed rate. The setup design is based on a theoretical framework incorporating optical calculations. These calculations indicating the asteroid model needed to be scaled down by a factor of 3192 to fit inside the camera depth-of-view. This resulted in a scaled Bennu asteroid with a size of 16.44 centimeters.The cameras under testing conducted three experiments to generate data sets. The utilization of a feature detection and tracking algorithm on both camera data sets revealed that the absolute number of tracked features, computation time, and robustness in various scenarios of the frame-based camera algorithm outperforms the event-based camera algorithm. However, when considering the percentages of tracked features relative to the total detected features, the event-based algorithm tracks a significantly higher percentage of features for at least one key frame than the frame-based algorithm.  The comparative analysis of the experiments performed in space-simulated conditions during this project showed that the feasibility of an event-based camera using solely events is low compared to the frame-based camera.

Radar Sounders (RSs) are active sensors that transmit in the nadir electromagnetic (EM) waves with a low frequency in the range of High-Frequency and Very-High-Frequency and relatively wide bandwidth. Such a signal penetrates the surface and propagates in the subsurface, interacting with dielectric interfaces. This interaction yields to backscattered echoes detectable by the antenna that are coherently summed and stored in radargrams. RSs are used for planetary exploration and Earth observation for their value in investigating subsurface geological structures and processes, which reveal the past geomorphological history and possible future evolution. RS instruments have several parameter configurations that have to be designed to achieve the mission science goals. On Mars, radargram visual analyses revealed the icy layered deposits and liquid water evidence in the poles. On the Earth, RSs showed relevant structures and processes in the cryosphere and the arid areas that help to monitor the subsurface geological evolution, which is critical for climate change. Despite the valuable results, visual analysis is subjective and not feasible for processing a large amount of data. Therefore, a need emerges for automatic methods extracting fast and reliable information from radargrams. The thesis addresses two main open issues of the radar-sounding literature: i) assessing target detectability in simulated orbiting radargrams to guide the design of RS instruments, and ii) designing automatic methods for information extraction from RS data. The RS design is based on assessing the performance of a given instrument parameter configuration in achieving the mission science goals and detecting critical targets. The assessment guides the parameter selection by determining the appropriate trade-off between the achievable performance and technical limitations. We propose assessing the detectability of subsurface targets (e.g., englacial layering and basal interface) from satellite radar sounders with novel performance metrics. This performance assessment strategy can be applied to guide the design of the SNR budget at the surface, which can further support the selection of the main EORS instrument parameters. The second contribution is designing automatic methods for analyzing radargrams based on fuzzy logic and deep learning. The first method aims at identifying buried cavities, such as lava tubes, exploiting their geometric and EM models. A fuzzy system is built on the model that detects candidate reflections from the surface and the lava tube boundary. The second and third proposed methods are based on deep learning, as they showed groundbreaking results in several applications. We contributed with an automatic technique for analyzing radargram acquired in icy areas to investigate the basal layer. To this end, radargrams are segmented with a deep learning network into literature classes, including englacial layers, bedrock, and echo-free zone (EFZ) and thermal noise, as well as new classes of basal ice and signal perturbation. The third method proposes an unsupervised segmentation of radargrams with deep learning for detecting subsurface features. Qualitative and quantitative experimental results obtained on planetary and terrestrial radargrams confirm the effectiveness of the proposed methods, which investigate new subsurface targets and allow an improvement in terms of accuracy when compared to other state-of-the-art methods.

Diese Arbeit handelt von der Anwendbarkeit laserinduzierter Plasmaspektroskopie (englisch: laser-induced breakdown spectroscopy, LIBS) mit Detektion im vakuumultravioletten Spektralbereich (VUV), im Folgenden VUV-LIBS genannt, im Bereich der Planetenforschung. Für LIBS wird ein gepulster Laser auf die zu untersuchende Probe fokussiert. Dabei wird Probenmaterial abgetragen, verdampft und teilweise ionisiert. Die im Plasma enthaltenen Atome und Ionen werden elektronisch angeregt und strahlen in der Folge Licht charakteristischer Wellenlängen ab, welches spektroskopisch analysiert werden kann. Diese Analyse erlaubt einen Rückschluss auf die im Plasma und somit in der Probe enthaltenen chemischen Elemente. Mit LIBS können alle Elemente detektiert werden. Allerdings sind insbesondere die Nichtmetalle schwerer zu detektieren, deren intensivste Emissionslinien im VUV-Bereich liegen, d.h. bei Wellenlängen kürzer als 200 nm, der oft nicht untersucht wird. In diesem Spektralbereich wird ein Großteil der Strahlung von der irdischen Atmosphäre absorbiert. Auf atmosphärelosen Himmelskörpern wie dem Mond ist dies nicht der Fall, sodass für die Elemente C, Cl, H, N, O, P und S eine verbesserte Detektierbarkeit erwartet wird als mit konventionellem LIBS im typischerweise untersuchten Spektralbereich über 200 nm. Die hier präsentierten Ergebnisse deuten darauf hin, dass VUV-LIBS in der Tat eine verbesserte Detektierbarkeit für S und Cl im Kontext einer Mondmission bewirken kann. Für eine umfassende Beurteilung der Methode in dieser Anwendung und zur Verbesserung der Nachweisgrenzen sind jedoch weitere Untersuchungen mit einem verbesserten Messaufbau notwendig. Da wichtige gesteinsbildende Elemente wie Ca, Na und Mg im VUV-Bereich keine oder nur schwache Emission zeigen, liegt das größte Potenzial von VUV-LIBS möglicherweise in der Kombination mit LIBS in anderen Spektralbereichen oder mit anderen analytischen Methoden. / This thesis investigates the application of laser-induced breakdown spectroscopy (LIBS) with detection in the vacuum ultraviolet (VUV) spectral range for in-situ space exploration. For LIBS, a pulsed laser is tightly focused onto the sample, thereby ablating material and exciting a luminous plasma. The atoms and ions contained in the plasma radiate light of characteristic wavelengths, which can be analysed with spectrometers. The spectral analysis allows to identify the chemical elements in the plasma, which are assumed to be representative for the elements contained in the sample. With LIBS, all elements can be detected. However, especially the non-metal elements are challenging to detect because their strongest lines are located in the VUV spectral range, i.e. below 200 nm, which is often not investigated. Detection in this range brings its own challenges, since large parts of the radiation spectrum are absorbed by the atmosphere surrounding the sample. On celestial bodies without an atmosphere, such as the Moon, the ambient conditions are well suited for VUV-LIBS analyses. In such a scenario, a better detectability for the otherwise challenging elements C, Cl, H, N, O, P and S is expected compared to LIBS in the usually employed detection range above 200 nm. The results shown in this thesis indicate that VUV-LIBS is promising for the improved detection of light elements such as S and Cl in a lunar context. However, more extensive studies with an optimized set-up are necessary to properly assess the true capabilities of the method and to further reduce the detection limits. Although emission from the most abundant chemical elements in geological samples, Al, Si and O, could be reliably detected in all samples containing them, VUV-LIBS might in the end be best used in combination with LIBS in the UV-VIS range or with other analytical techniques, because the major rock forming elements Ca, Na and Mg hardly show emission lines in the VUV spectral range.

This thesis aimed to assess the feasibility of using an off-the-shelf virtual reality tracking system as a low cost precision pose estimation solution for robotic operations in both indoor and outdoor environments. Such a tracking solution has the potential of assisting critical operations related to planetary exploration missions, parcel handling/delivery, and wildfire detection/early warning systems. The boom of virtual reality experiences has accelerated the development of various low-cost, precision indoor tracking technologies. For the purpose of this thesis we choose to adapt the SteamVR Lighthouse system developed by Valve, which uses photo-diodes on the trackers to detect the rotating IR laser sheets emitted from the anchored base stations, also known as lighthouses. Some previous researches had been completed using the first generation of lighthouses, which has a few limitations on communication from lighthouses to the tracker. A NASA research has cited poor tracking performance under sunlight. We choose to use the second generation lighthouses which has improved the method of communication from lighthouses to the tracker, and we performed various experiments to assess their performance outdoors, including under sunlight. The studies of this thesis have two stages, the first stage focused on a controlled, indoor environment, having an Unmanned Aerial Vehicle (UAS) perform repeatable flight patterns and simultaneously tracked by the Lighthouse and a reference indoor tracking system, which showed that the tracking precision of the lighthouse is comparable to the industrial standard indoor tracking solution. The second stage of the study focused on outdoor experiments with the tracking system, comparing UAS flights between day and night conditions as well as positioning accuracy assessments with a CNC machine under indoor and outdoor conditions. The results showed matching performance between day and night while still comparable to industrial standard indoor tracking solution down to centimeter precision, and matching simulated CNC trajectory down to millimeter precision. There is also some room for improvement in regards to the experimental method and equipment used, as well as improvements on the tracking system itself needed prior to adaptation in real-world applications. / Master of Science / This thesis aimed to assess the feasibility of using an off-the-shelf virtual reality tracking system as a low cost precision pose estimation solution for robotic operations in both indoor and outdoor environments. Such a tracking solution has the potential of assisting critical operations related to planetary exploration missions, parcel handling/delivery, and wildfire detection/early warning systems. The boom of virtual reality experiences has accelerated the development of various low-cost, precision indoor tracking technologies. For the purpose of this thesis we choose to adapt the SteamVR Lighthouse system developed by Valve, which uses photo-diodes on the trackers to detect the rotating IR laser sheets emitted from the anchored base stations, also known as lighthouses. Some previous researches had been completed using the first generation of lighthouses, which has a few limitations on communication from lighthouses to the tracker. A NASA research has cited poor tracking performance under sunlight. We choose to use the second generation lighthouses which has improved the method of communication from lighthouses to the tracker, and we performed various experiments to assess their performance outdoors, including under sunlight. The studies of this thesis have two stages, the first stage focused on a controlled, indoor environment, having an Unmanned Aerial Vehicle (UAS) perform repeatable flight patterns and simultaneously tracked by the Lighthouse and a reference indoor tracking system, which showed that the tracking precision of the lighthouse is comparable to the industrial standard indoor tracking solution. The second stage of the study focused on outdoor experiments with the tracking system, comparing UAS flights between day and night conditions as well as positioning accuracy assessments with a CNC machine under indoor and outdoor conditions. The results showed matching performance between day and night while still comparable to industrial standard indoor tracking solution down to centimeter precision, and matching simulated CNC trajectory down to millimeter precision. There is also some room for improvement in regards to the experimental method and equipment used, as well as improvements on the tracking system itself needed prior to adaptation in real-world applications.

This thesis addresses the problem of improving the delineation of skylines, also referred to as skyline detection, in occluded and challenging environments where existing skyline delineation methods may struggle or fail. Delineated skylines can be used in monocular camera localization methods by comparing delineated skylines to digital elevation model data to estimate a position based on known terrain. This is particularly useful in GPS-denied environments in which active sensing is either impractical or undesirable for various reasons, so that passive sensing using monocular cameras is necessary and/or strategically advantageous. This thesis presents a novel method of skyline delineation using deep learning-based semantic segmentation of monocular camera images to detect natural skylines of distant landscapes in the presence of occlusions. Skylines are extracted from semantic segmentation predictions as the boundary between pixel clusters labeled as terrain to those labeled as sky, with additional segmentation classes representing the known set of potential occlusions in a given environment. Additionally, each pixel in the detected skyline contours are assigned a confidence score based on local intensity gradients to reduce the potential impacts of erroneous skyline contours on position estimation. The utility of these delineated skylines is demonstrated by obtaining orientation and position estimates using existing methods of skyline-based localization. In these methods, the delineated natural skyline is compared to rendered skylines using digital elevation model data and the position estimate is obtained by finding the closest match. Results from the proposed skyline delineation method using semantic segmentation, with accompanying localization demonstration, is presented on two distinct data sets. The first is obtained from the Perseverance Rover operating in the Jezero Crater region of Mars, and the second is obtained from an uncrewed surface vessel operating in the Gulf of Koper, Slovenia. / Denna avhandling tar upp problemet med att förbättra avgränsningen av skylines, även kallad skylinedetektion, i tilltäppta och utmanande miljöer där befintliga skylineavgränsningsmetoder kan kämpa eller misslyckas. Avgränsade skylines kan användas i monokulära kameralokaliseringsmetoder genom att jämföra avgränsade skylines med digitala höjdmodelldata för att uppskatta en position baserat på känd terräng. Detta är särskilt användbart i GPS-nekas miljöer där aktiv avkänning är antingen opraktisk eller oönskad av olika skäl, så att passiv avkänning med användning av monokulära kameror är nödvändig och/eller strategiskt fördelaktig. Denna avhandling presenterar en ny metod för skylineavgränsning med användning av djupinlärningsbaserad semantisk segmentering av monokulära kamerabilder för att detektera naturliga skylines av avlägsna landskap i närvaro av ocklusioner. Horisonter extraheras från semantiska segmenteringsförutsägelser som gränsen mellan pixelkluster märkta som terräng till de märkta som himmel, med ytterligare segmenteringsklasser som representerar den kända uppsättningen potentiella ocklusioner i en given miljö. Dessutom tilldelas varje pixel i de detekterade skylinekonturerna ett konfidenspoäng baserat på lokala intensitetsgradienter för att minska den potentiella påverkan av felaktiga skylinekonturer på positionsuppskattning. Användbarheten av dessa avgränsade skylines demonstreras genom att erhålla orienterings- och positionsuppskattningar med hjälp av befintliga metoder för skylinebaserad lokalisering. I dessa metoder jämförs den avgränsade naturliga horisonten med renderade silhuetter med hjälp av digitala höjdmodelldata och positionsuppskattningen erhålls genom att hitta den närmaste matchningen. Resultat från den föreslagna metoden för skylineavgränsning med semantisk segmentering, med tillhörande lokaliseringsdemonstration, presenteras på två distinkta datamängder. Den första kommer från Perseverance Rover som verkar i Jezero Crater-regionen på Mars, och den andra erhålls från ett obemannat ytfartyg som verkar i Koperbukten, Slovenien.

No / The ESA/Roscosmos ExoMars rover will be launched in 2018. The primary aim of the mission will be to find evidence of extinct or extant life by extracting samples from the subsurface of Mars. The rover will incorporate a drill that is capable of extracting cores from depths of up to 2 m, a Sample Preparation and Distribution System (SPDS) that will crush the core into small grains and a suite of analytical instruments. A key component of the analytical suite will be the Raman Laser Spectrometer (RLS) that will be used to probe the molecular and mineralogical composition of the samples. In this work we consider the capability of the proposed Raman spectrometer to detect reduced carbon (possibly associated with evidence for extinct life) and to identify the level of thermal alteration/maturity. The Raman analysis of 21 natural samples of shale (originating from regions exhibiting different levels of thermal maturity) is described and it is shown that reduced carbon levels as low as 0.08% can be readily detected. It is also demonstrated that the Raman spectra obtained with the instrument can be used to distinguish between samples exhibiting high and low levels of thermal maturity and that reduced carbon can be detected in samples exposed to significant levels of oxidation (as expected on the surface of Mars). (C) 2014 Published by Elsevier Ltd.