Precise and Robust Visual SLAM with Inertial Sensors and Deep Learning.

Dotar a los robots con el sentido de la percepción destaca como el componente más importante para conseguir máquinas completamente autónomas. Una vez que las máquinas sean capaces de percibir el mundo, podrán interactuar con él. A este respecto, la localización y la reconstrucción de mapas de manera simultánea, SLAM (por sus siglas en inglés) comprende todas las técnicas que permiten a los robots estimar su posición y reconstruir el mapa de su entorno al mismo tiempo, usando únicamente el conjunto de sensores a bordo. El SLAM constituye el elemento clave para la percepción de las máquinas, estando ya presente en diferentes tecnologías y aplicaciones como la conducción autónoma, la realidad virtual y aumentada o los robots de servicio. Incrementar la robustez del SLAM expandiría su uso y aplicación, haciendo las máquinas más seguras y requiriendo una menor intervención humana.En esta tesis hemos combinado sensores inerciales (IMU) y visuales para incrementar la robustez del SLAM ante movimientos rápidos, oclusiones breves o entornos con poca textura. Primero hemos propuesto dos técnicas rápidas para la inicialización del sensor inercial, con un bajo error de escala. Estas han permitido empezar a usar la IMU tan pronto como 2 segundos después de lanzar el sistema. Una de estas inicializaciones ha sido integrada en un nuevo sistema de SLAM visual inercial, acuñado como ORB-SLAM3, el cual representa la mayor contribución de esta tesis. Este es el sistema de SLAM visual-inercial de código abierto más completo hasta la fecha, que funciona con cámaras monoculares o estéreo, estenopeicas o de ojo de pez, y con capacidades multimapa. ORB-SLAM3 se basa en una formulación de Máximo a Posteriori, tanto en la inicialización como en el refinamiento y el ajuste de haces visual-inercial. También explota la asociación de datos en el corto, medio y largo plazo. Todo esto hace que ORB-SLAM3 sea el sistema SLAM visual-inercial más preciso, como así demuestran nuestros resultados en experimentos públicos.Además, hemos explorado la aplicación de técnicas de aprendizaje profundo para mejorar la robustez del SLAM. En este aspecto, primero hemos propuesto DynaSLAM II, un sistema SLAM estéreo para entornos dinámicos. Los objetos dinámicos son segmentados mediante una red neuronal, y sus puntos y medidas son incluidas eficientemente en la optimización de ajuste de haces. Esto permite estimar y hacer seguimiento de los objetos en movimiento, al mismo tiempo que se mejora la estimación de la trayectoria de la cámara. En segundo lugar, hemos desarrollado un SLAM monocular y directo basado en predicciones de profundidad a través de redes neuronales. Optimizamos de manera conjunta tanto los residuos de predicción de profundidad como los fotométricos de distintas vistas, lo que da lugar a un sistema monocular capaz de estimar la escala. No sufre el problema de deriva de escala, siendo más robusto y varias veces más preciso que los sistemas monoculares clásicos.<br /

Robust Visual SLAM in Challenging Environments with Low-texture and Dynamic Illumination

- Robustness to Dynamic Illumination conditions is also one of the main open challenges in visual odometry and SLAM, e.g. high dynamic range (HDR) environments. The main difficulties in these situations come from both the limitations of the sensors, for instance automatic settings of a camera might not react fast enough to properly record dynamic illumination changes, and also from limitations in the algorithms, e.g. the track of interest points is typically based on brightness constancy. The work of this thesis contributes to mitigate these phenomena from two different perspectives. The first one addresses this problem from a deep learning perspective by enhancing images to invariant and richer representations for VO and SLAM, benefiting from the generalization properties of deep neural networks. In this work it is also demonstrated how the insertion of long short term memory (LSTM) allows us to obtain temporally consistent sequences, since the estimation depends on previous states. Secondly, a more traditional perspective is exploited to contribute with a purely geometric-based tracking of line segments in challenging stereo streams with complex or varying illumination, since they are intrinsically more informative. Fecha de lectura de Tesis Doctoral: 26 de febrero 2020In the last years, visual Simultaneous Localization and Mapping (SLAM) has played a role of capital importance in rapid technological advances, e.g. mo- bile robotics and applications such as virtual, augmented, or mixed reality (VR/AR/MR), as a vital part of their processing pipelines. As its name indicates, it comprises the estimation of the state of a robot (typically the pose) while, simultaneously, incrementally building and refining a consistent representation of the environment, i.e. the so-called map, based on the equipped sensors. Despite the maturity reached by state-of-art visual SLAM techniques in controlled environments, there are still many open challenges to address be- fore reaching a SLAM system robust to long-term operations in uncontrolled scenarios, where classical assumptions, such as static environments, do not hold anymore. This thesis contributes to improve robustness of visual SLAM in harsh or difficult environments, in particular: - Low-textured Environments, where traditional approaches suffer from an accuracy impoverishment and, occasionally, the absolute failure of the system. Fortunately, many of such low-textured environments contain planar elements that are rich in linear shapes, so an alternative feature choice such as line segments would exploit information from structured parts of the scene. This set of contributions exploits both type of features, i.e. points and line segments, to produce visual odometry and SLAM algorithms robust in a broader variety of environments, hence leveraging them at all instances of the related processes: monocular depth estimation, visual odometry, keyframe selection, bundle adjustment, loop closing, etc. Additionally, an open-source C++ implementation of the proposed algorithms has been released along with the published articles and some extra multimedia material for the benefit of the community

Comparison of state marginalization techniques in visual inertial navigation filters

The main focus of this thesis is finding and validating an efficient visual inertial navigation system (VINS) algorithm for applications in micro aerial vehicles (MAV). A typical VINS for a MAV consists of a low-cost micro electro mechanical system (MEMS) inertial measurement unit (IMU) and a monocular camera, which provides a minimum payload sensor setup. This setup is highly desirable for navigation of MAVs because highly resource constrains in the platform. However, bias and noise of lowcost IMUs demand sufficiently accurate VINS algorithms. Accurate VINS algorithms has been developed over the past decade but they demand higher computational resources. Therefore, resource limited MAVs demand computationally efficient VINS algorithms. This thesis considers the following computational cost elements in the VINS algorithm: feature tracking front-end, state marginalization technique and the complexity of the algorithm formulation. In this thesis three state-of-the-art feature tracking front ends were compared in terms of accuracy. (VINS-Mono front-end, MSCKF-Mono feature tracker and Matlab based feature tracker). Four state-ofthe- art state marginalization techniques (MSCKF-Generic marginalization, MSCKFMono marginalization, MSCKF-Two way marginalization and Two keyframe based epipolar constraint marginalization) were compared in terms of accuracy and efficiency. The complexity of the VINS algorithm formulation has also been compared using the filter execution time. The research study then presents the comparative analysis of the algorithms using a publicly available MAV benchmark datasets. Based on the results, an efficient VINS algorithm is proposed which is suitable for MAVs

Single and multiple stereo view navigation for planetary rovers

© Cranfield UniversityThis thesis deals with the challenge of autonomous navigation of the ExoMars rover. The absence of global positioning systems (GPS) in space, added to the limitations of wheel odometry makes autonomous navigation based on these two techniques - as done in the literature - an inviable solution and necessitates the use of other approaches. That, among other reasons, motivates this work to use solely visual data to solve the robot’s Egomotion problem. The homogeneity of Mars’ terrain makes the robustness of the low level image processing technique a critical requirement. In the first part of the thesis, novel solutions are presented to tackle this specific problem. Detection of robust features against illumination changes and unique matching and association of features is a sought after capability. A solution for robustness of features against illumination variation is proposed combining Harris corner detection together with moment image representation. Whereas the first provides a technique for efficient feature detection, the moment images add the necessary brightness invariance. Moreover, a bucketing strategy is used to guarantee that features are homogeneously distributed within the images. Then, the addition of local feature descriptors guarantees the unique identification of image cues. In the second part, reliable and precise motion estimation for the Mars’s robot is studied. A number of successful approaches are thoroughly analysed. Visual Simultaneous Localisation And Mapping (VSLAM) is investigated, proposing enhancements and integrating it with the robust feature methodology. Then, linear and nonlinear optimisation techniques are explored. Alternative photogrammetry reprojection concepts are tested. Lastly, data fusion techniques are proposed to deal with the integration of multiple stereo view data. Our robust visual scheme allows good feature repeatability. Because of this, dimensionality reduction of the feature data can be used without compromising the overall performance of the proposed solutions for motion estimation. Also, the developed Egomotion techniques have been extensively validated using both simulated and real data collected at ESA-ESTEC facilities. Multiple stereo view solutions for robot motion estimation are introduced, presenting interesting benefits. The obtained results prove the innovative methods presented here to be accurate and reliable approaches capable to solve the Egomotion problem in a Mars environment

Towards Robust Dense Visual Simultaneous Localization and Mapping (SLAM)

Long-term autonomy is the dream of many roboticists – and if a robotic system can be split into three main categories: perception, planning and control – then the biggest challenges to achieve this dream are undoubtedly faced in perception. Large scale environments that change with time – due to normal operations or even lighting changes – are typical situations the robot would encounter. As such, a Simultaneous Localization and Mapping (SLAM) system that is robust enough to handle many of these conditions is desired.The objective of this dissertation is to present components that would lead to a robust dense visual SLAM system. It starts by exploring 3D reconstruction algorithms, showing distinctions between local and global methods and presenting an incremental and adaptive global method designed to create depth maps as a robot navigates in space.It then introduces the concept of sensor fusion, where multiple sensors are joined to provide a higher degree of tracking accuracy. It compares different visual SLAM systems – dense and semi-dense – and shows how the inclusion of an Inertial Measurement Unit (IMU) aids considerably in tracking. It then uses this localization framework in a large scale volumetric mapping system, and shows results for both indoor and outdoor environments using real world datasets.Finally, it explores different error metrics used in direct photometric optimization – the foundation of dense tracking systems. It introduces the Normalized Information Distance (NID), an entropy based metric that is shown to achieve high localization success rate and accuracy even in the face of extreme lighting differences

Binokulare Eigenbewegungsschätzung für Fahrerassistenzanwendungen

Driving can be dangerous. Humans become inattentive when performing a monotonous task like driving. Also the risk implied while multi-tasking, like using the cellular phone while driving, can break the concentration of the driver and increase the risk of accidents. Others factors like exhaustion, nervousness and excitement affect the performance of the driver and the response time. Consequently, car manufacturers have developed systems in the last decades which assist the driver under various circumstances. These systems are called driver assistance systems. Driver assistance systems are meant to support the task of driving, and the field of action varies from alerting the driver, with acoustical or optical warnings, to taking control of the car, such as keeping the vehicle in the traffic lane until the driver resumes control. For such a purpose, the vehicle is equipped with on-board sensors which allow the perception of the environment and/or the state of the vehicle. Cameras are sensors which extract useful information about the visual appearance of the environment. Additionally, a binocular system allows the extraction of 3D information. One of the main requirements for most camera-based driver assistance systems is the accurate knowledge of the motion of the vehicle. Some sources of information, like velocimeters and GPS, are of common use in vehicles today. Nevertheless, the resolution and accuracy usually achieved with these systems are not enough for many real-time applications. The computation of ego-motion from sequences of stereo images for the implementation of driving intelligent systems, like autonomous navigation or collision avoidance, constitutes the core of this thesis. This dissertation proposes a framework for the simultaneous computation of the 6 degrees of freedom of ego-motion (rotation and translation in 3D Euclidean space), the estimation of the scene structure and the detection and estimation of independently moving objects. The input is exclusively provided by a binocular system and the framework does not call for any data acquisition strategy, i.e. the stereo images are just processed as they are provided. Stereo allows one to establish correspondences between left and right images, estimating 3D points of the environment via triangulation. Likewise, feature tracking establishes correspondences between the images acquired at different time instances. When both are used together for a large number of points, the result is a set of clouds of 3D points with point-to-point correspondences between clouds. The apparent motion of the 3D points between consecutive frames is caused by a variety of reasons. The most dominant motion for most of the points in the clouds is caused by the ego-motion of the vehicle; as the vehicle moves and images are acquired, the relative position of the world points with respect to the vehicle changes. Motion is also caused by objects moving in the environment. They move independently of the vehicle motion, so the observed motion for these points is the sum of the ego-vehicle motion and the independent motion of the object. A third reason, and of paramount importance in vision applications, is caused by correspondence problems, i.e. the incorrect spatial or temporal assignment of the point-to-point correspondence. Furthermore, all the points in the clouds are actually noisy measurements of the real unknown 3D points of the environment. Solving ego-motion and scene structure from the clouds of points requires some previous analysis of the noise involved in the imaging process, and how it propagates as the data is processed. Therefore, this dissertation analyzes the noise properties of the 3D points obtained through stereo triangulation. This leads to the detection of a bias in the estimation of 3D position, which is corrected with a reformulation of the projection equation. Ego-motion is obtained by finding the rotation and translation between the two clouds of points. This problem is known as absolute orientation, and many solutions based on least squares have been proposed in the literature. This thesis reviews the available closed form solutions to the problem. The proposed framework is divided in three main blocks: 1) stereo and feature tracking computation, 2) ego-motion estimation and 3) estimation of 3D point position and 3D velocity. The first block solves the correspondence problem providing the clouds of points as output. No special implementation of this block is required in this thesis. The ego-motion block computes the motion of the cameras by finding the absolute orientation between the clouds of static points in the environment. Since the cloud of points might contain independently moving objects and outliers generated by false correspondences, the direct computation of the least squares might lead to an erroneous solution. The first contribution of this thesis is an effective rejection rule that detects outliers based on the distance between predicted and measured quantities, and reduces the effects of noisy measurement by assigning appropriate weights to the data. This method is called Smoothness Motion Constraint (SMC). The ego-motion of the camera between two frames is obtained finding the absolute orientation between consecutive clouds of weighted 3D points. The complete ego-motion since initialization is achieved concatenating the individual motion estimates. This leads to a super-linear propagation of the error, since noise is integrated. A second contribution of this dissertation is a predictor/corrector iterative method, which integrates the clouds of 3D points of multiple time instances for the computation of ego-motion. The presented method considerably reduces the accumulation of errors in the estimated ego-position of the camera. Another contribution of this dissertation is a method which recursively estimates the 3D world position of a point and its velocity; by fusing stereo, feature tracking and the estimated ego-motion in a Kalman Filter system. An improved estimation of point position is obtained this way, which is used in the subsequent system cycle resulting in an improved computation of ego-motion. The general contribution of this dissertation is a single framework for the real time computation of scene structure, independently moving objects and ego-motion for automotive applications.Autofahren kann gefährlich sein. Die Fahrleistung wird durch die physischen und psychischen Grenzen des Fahrers und durch externe Faktoren wie das Wetter beeinflusst. Fahrerassistenzsysteme erhöhen den Fahrkomfort und unterstützen den Fahrer, um die Anzahl an Unfällen zu verringern. Fahrerassistenzsysteme unterstützen den Fahrer durch Warnungen mit optischen oder akustischen Signalen bis hin zur Übernahme der Kontrolle über das Auto durch das System. Eine der Hauptvoraussetzungen für die meisten Fahrerassistenzsysteme ist die akkurate Kenntnis der Bewegung des eigenen Fahrzeugs. Heutzutage verfügt man über verschiedene Sensoren, um die Bewegung des Fahrzeugs zu messen, wie zum Beispiel GPS und Tachometer. Doch Auflösung und Genauigkeit dieser Systeme sind nicht ausreichend für viele Echtzeitanwendungen. Die Berechnung der Eigenbewegung aus Stereobildsequenzen für Fahrerassistenzsysteme, z.B. zur autonomen Navigation oder Kollisionsvermeidung, bildet den Kern dieser Arbeit. Diese Dissertation präsentiert ein System zur Echtzeitbewertung einer Szene, inklusive Detektion und Bewertung von unabhängig bewegten Objekten sowie der akkuraten Schätzung der sechs Freiheitsgrade der Eigenbewegung. Diese grundlegenden Bestandteile sind erforderlich, um viele intelligente Automobilanwendungen zu entwickeln, die den Fahrer in unterschiedlichen Verkehrssituationen unterstützen. Das System arbeitet ausschließlich mit einer Stereokameraplattform als Sensor. Um die Eigenbewegung und die Szenenstruktur zu berechnen wird eine Analyse des Rauschens und der Fehlerfortpflanzung im Bildaufbereitungsprozess benötigt. Deshalb werden in dieser Dissertation die Rauscheigenschaften der durch Stereotriangulation erhaltenen 3D-Punkte analysiert. Dies führt zu der Entdeckung eines systematischen Fehlers in der Schätzung der 3D-Position, der sich mit einer Neuformulierung der Projektionsgleichung korrigieren lässt. Die Simulationsergebnisse zeigen, dass eine bedeutende Verringerung des Fehlers in der geschätzten 3D-Punktposition möglich ist. Die Eigenbewegungsschätzung wird gewonnen, indem die Rotation und Translation zwischen Punktwolken geschätzt wird. Dieses Problem ist als „absolute Orientierung” bekannt und viele Lösungen auf Basis der Methode der kleinsten Quadrate sind in der Literatur vorgeschlagen worden. Diese Arbeit rezensiert die verfügbaren geschlossenen Lösungen zu dem Problem. Das vorgestellte System gliedert sich in drei wesentliche Bausteine: 1. Registrierung von Bildmerkmalen, 2. Eigenbewegungsschätzung und 3. iterative Schätzung von 3D-Position und 3D-Geschwindigkeit von Weltpunkten. Der erster Block erhält eine Folge rektifizierter Bilder als Eingabe und liefert daraus eine Liste von verfolgten Bildmerkmalen mit ihrer entsprechenden 3D-Position. Der Block „Eigenbewegungsschätzung” besteht aus vier Hauptschritten in einer Schleife: 1. Bewegungsvorhersage, 2. Anwendung der Glattheitsbedingung für die Bewegung (GBB), 3. absolute Orientierungsberechnung und 4. Bewegungsintegration. Die in dieser Dissertation vorgeschlagene GBB ist eine mächtige Bedingung für die Ablehnung von Ausreißern und für die Zuordnung von Gewichten zu den gemessenen 3D-Punkten. Simulationen werden mit gaußschem und slashschem Rauschen ausgeführt. Die Ergebnisse zeigen die Überlegenheit der GBB-Version über die Standardgewichtungsmethoden. Die Stabilität der Ergebnisse hinsichtlich Ausreißern wurde analysiert mit dem Resultat, dass der „break down point” größer als 50% ist. Wenn die vier Schritte iterativ ausgeführt, werden wird ein Prädiktor-Korrektor-Verfahren gewonnen.Wir nennen diese Schätzung Multi-frameschätzung im Gegensatz zur Zweiframeschätzung, die nur die aktuellen und vorherigen Bildpaare für die Berechnung der Eigenbewegung betrachtet. Die erste Iteration wird zwischen der aktuellen und vorherigen Wolke von Punkten durchgeführt. Jede weitere Iteration integriert eine zusätzliche Punktwolke eines vorherigen Zeitpunkts. Diese Methode reduziert die Fehlerakkumulation bei der Integration von mehreren Schätzungen in einer einzigen globalen Schätzung. Simulationsergebnisse zeigen, dass obwohl der Fehler noch superlinear im Laufe der Zeit zunimmt, die Größe des Fehlers um mehrere Größenordnungen reduziert wird. Der dritte Block besteht aus der iterativen Schätzung von 3D-Position und 3D-Geschwindigkeit von Weltpunkten. Hier wird eine Methode basierend auf einem Kalman Filter verwendet, das Stereo, Featuretracking und Eigenbewegungsdaten fusioniert. Messungen der Position eines Weltpunkts werden durch das Stereokamerasystem gewonnen. Die Differenzierung der Position des geschätzten Punkts erlaubt die zusätzliche Schätzung seiner Geschwindigkeit. Die Messungen werden durch das Messmodell gewonnen, das Stereo- und Bewegungsdaten fusioniert. Simulationsergebnisse validieren das Modell. Die Verringerung der Positionsunsicherheit im Laufe der Zeit wird mit einer Monte-Carlo Simulation erzielt. Experimentelle Ergebnisse werden mit langen Sequenzen von Bildern erzielt. Zusätzliche Tests, einschließlich einer 3D-Rekonstruktion einer Waldszene und der Berechnung der freien Kamerabewegung in einem Indoor-Szenario, wurden durchgeführt. Die Methode zeigt gute Ergebnisse in allen Fällen. Der Algorithmus liefert zudem akzeptable Ergebnisse bei der Schätzung der Lage kleiner Objekte, wie Köpfe und Beine von realen Crash-Test-Dummies

GO-SLAM: Global Optimization for Consistent 3D Instant Reconstruction

Neural implicit representations have recently demonstrated compelling results on dense Simultaneous Localization And Mapping (SLAM) but suffer from the accumulation of errors in camera tracking and distortion in the reconstruction. Purposely, we present GO-SLAM, a deep-learning-based dense visual SLAM framework globally optimizing poses and 3D reconstruction in real-time. Robust pose estimation is at its core, supported by efficient loop closing and online full bundle adjustment, which optimize per frame by utilizing the learned global geometry of the complete history of input frames. Simultaneously, we update the implicit and continuous surface representation on-the-fly to ensure global consistency of 3D reconstruction. Results on various synthetic and real-world datasets demonstrate that GO-SLAM outperforms state-of-the-art approaches at tracking robustness and reconstruction accuracy. Furthermore, GO-SLAM is versatile and can run with monocular, stereo, and RGB-D input.Comment: ICCV 2023. Code: https://github.com/youmi-zym/GO-SLAM - Project Page: https://youmi-zym.github.io/projects/GO-SLAM

Robust convex optimisation techniques for autonomous vehicle vision-based navigation

This thesis investigates new convex optimisation techniques for motion and pose estimation. Numerous computer vision problems can be formulated as optimisation problems. These optimisation problems are generally solved via linear techniques using the singular value decomposition or iterative methods under an L2 norm minimisation. Linear techniques have the advantage of offering a closed-form solution that is simple to implement. The quantity being minimised is, however, not geometrically or statistically meaningful. Conversely, L2 algorithms rely on iterative estimation, where a cost function is minimised using algorithms such as Levenberg-Marquardt, Gauss-Newton, gradient descent or conjugate gradient. The cost functions involved are geometrically interpretable and can statistically be optimal under an assumption of Gaussian noise. However, in addition to their sensitivity to initial conditions, these algorithms are often slow and bear a high probability of getting trapped in a local minimum or producing infeasible solutions, even for small noise levels. In light of the above, in this thesis we focus on developing new techniques for finding solutions via a convex optimisation framework that are globally optimal. Presently convex optimisation techniques in motion estimation have revealed enormous advantages. Indeed, convex optimisation ensures getting a global minimum, and the cost function is geometrically meaningful. Moreover, robust optimisation is a recent approach for optimisation under uncertain data. In recent years the need to cope with uncertain data has become especially acute, particularly where real-world applications are concerned. In such circumstances, robust optimisation aims to recover an optimal solution whose feasibility must be guaranteed for any realisation of the uncertain data. Although many researchers avoid uncertainty due to the added complexity in constructing a robust optimisation model and to lack of knowledge as to the nature of these uncertainties, and especially their propagation, in this thesis robust convex optimisation, while estimating the uncertainties at every step is investigated for the motion estimation problem. First, a solution using convex optimisation coupled to the recursive least squares (RLS) algorithm and the robust H filter is developed for motion estimation. In another solution, uncertainties and their propagation are incorporated in a robust L convex optimisation framework for monocular visual motion estimation. In this solution, robust least squares is combined with a second order cone program (SOCP). A technique to improve the accuracy and the robustness of the fundamental matrix is also investigated in this thesis. This technique uses the covariance intersection approach to fuse feature location uncertainties, which leads to more consistent motion estimates. Loop-closure detection is crucial in improving the robustness of navigation algorithms. In practice, after long navigation in an unknown environment, detecting that a vehicle is in a location it has previously visited gives the opportunity to increase the accuracy and consistency of the estimate. In this context, we have developed an efficient appearance-based method for visual loop-closure detection based on the combination of a Gaussian mixture model with the KD-tree data structure. Deploying this technique for loop-closure detection, a robust L convex posegraph optimisation solution for unmanned aerial vehicle (UAVs) monocular motion estimation is introduced as well. In the literature, most proposed solutions formulate the pose-graph optimisation as a least-squares problem by minimising a cost function using iterative methods. In this work, robust convex optimisation under the L norm is adopted, which efficiently corrects the UAV’s pose after loop-closure detection. To round out the work in this thesis, a system for cooperative monocular visual motion estimation with multiple aerial vehicles is proposed. The cooperative motion estimation employs state-of-the-art approaches for optimisation, individual motion estimation and registration. Three-view geometry algorithms in a convex optimisation framework are deployed on board the monocular vision system for each vehicle. In addition, vehicle-to-vehicle relative pose estimation is performed with a novel robust registration solution in a global optimisation framework. In parallel, and as a complementary solution for the relative pose, a robust non-linear H solution is designed as well to fuse measurements from the UAVs’ on-board inertial sensors with the visual estimates. The suggested contributions have been exhaustively evaluated over a number of real-image data experiments in the laboratory using monocular vision systems and range imaging devices. In this thesis, we propose several solutions towards the goal of robust visual motion estimation using convex optimisation. We show that the convex optimisation framework may be extended to include uncertainty information, to achieve robust and optimal solutions. We observed that convex optimisation is a practical and very appealing alternative to linear techniques and iterative methods