Continuous fusion of motion data using an axis-angle rotation representation with uniform B-spline

The fusion of motion data is key in the fields of robotic and automated driving. Most existing approaches are filter-based or pose-graph-based. By using filter-based approaches, parameters should be set very carefully and the motion data can usually only be fused in a time forward direction. Pose-graph-based approaches can fuse data in time forward and backward directions. However, pre-integration is needed by applying measurements from inertial measurement units. Additionally, both approaches only provide discrete fusion results. In this work, we address this problem and present a uniform B-spline-based continuous fusion approach, which can fuse motion measurements from an inertial measurement unit and pose data from other localization systems robustly, accurately and efficiently. In our continuous fusion approach, an axis-angle is applied as our rotation representation method and uniform B-spline as the back-end optimization base. Evaluation results performed on the real world data show that our approach provides accurate, robust and continuous fusion results, which again supports our continuous fusion concept

A Comprehensive Introduction of Visual-Inertial Navigation

In this article, a tutorial introduction to visual-inertial navigation(VIN) is presented. Visual and inertial perception are two complementary sensing modalities. Cameras and inertial measurement units (IMU) are the corresponding sensors for these two modalities. The low cost and light weight of camera-IMU sensor combinations make them ubiquitous in robotic navigation. Visual-inertial Navigation is a state estimation problem, that estimates the ego-motion and local environment of the sensor platform. This paper presents visual-inertial navigation in the classical state estimation framework, first illustrating the estimation problem in terms of state variables and system models, including related quantities representations (Parameterizations), IMU dynamic and camera measurement models, and corresponding general probabilistic graphical models (Factor Graph). Secondly, we investigate the existing model-based estimation methodologies, these involve filter-based and optimization-based frameworks and related on-manifold operations. We also discuss the calibration of some relevant parameters, also initialization of state of interest in optimization-based frameworks. Then the evaluation and improvement of VIN in terms of accuracy, efficiency, and robustness are discussed. Finally, we briefly mention the recent development of learning-based methods that may become alternatives to traditional model-based methods.Comment: 35 pages, 10 figure

Safety-quantifiable Line Feature-based Monocular Visual Localization with 3D Prior Map

Accurate and safety-quantifiable localization is of great significance for safety-critical autonomous systems, such as unmanned ground vehicles (UGV) and unmanned aerial vehicles (UAV). The visual odometry-based method can provide accurate positioning in a short period but is subjected to drift over time. Moreover, the quantification of the safety of the localization solution (the error is bounded by a certain value) is still a challenge. To fill the gaps, this paper proposes a safety-quantifiable line feature-based visual localization method with a prior map. The visual-inertial odometry provides a high-frequency local pose estimation which serves as the initial guess for the visual localization. By obtaining a visual line feature pair association, a foot point-based constraint is proposed to construct the cost function between the 2D lines extracted from the real-time image and the 3D lines extracted from the high-precision prior 3D point cloud map. Moreover, a global navigation satellite systems (GNSS) receiver autonomous integrity monitoring (RAIM) inspired method is employed to quantify the safety of the derived localization solution. Among that, an outlier rejection (also well-known as fault detection and exclusion) strategy is employed via the weighted sum of squares residual with a Chi-squared probability distribution. A protection level (PL) scheme considering multiple outliers is derived and utilized to quantify the potential error bound of the localization solution in both position and rotation domains. The effectiveness of the proposed safety-quantifiable localization system is verified using the datasets collected in the UAV indoor and UGV outdoor environments

Perception of Unstructured Environments for Autonomous Off-Road Vehicles

Autonome Fahrzeuge benötigen die Fähigkeit zur Perzeption als eine notwendige Voraussetzung für eine kontrollierbare und sichere Interaktion, um ihre Umgebung wahrzunehmen und zu verstehen. Perzeption für strukturierte Innen- und Außenumgebungen deckt wirtschaftlich lukrative Bereiche, wie den autonomen Personentransport oder die Industrierobotik ab, während die Perzeption unstrukturierter Umgebungen im Forschungsfeld der Umgebungswahrnehmung stark unterrepräsentiert ist. Die analysierten unstrukturierten Umgebungen stellen eine besondere Herausforderung dar, da die vorhandenen, natürlichen und gewachsenen Geometrien meist keine homogene Struktur aufweisen und ähnliche Texturen sowie schwer zu trennende Objekte dominieren. Dies erschwert die Erfassung dieser Umgebungen und deren Interpretation, sodass Perzeptionsmethoden speziell für diesen Anwendungsbereich konzipiert und optimiert werden müssen. In dieser Dissertation werden neuartige und optimierte Perzeptionsmethoden für unstrukturierte Umgebungen vorgeschlagen und in einer ganzheitlichen, dreistufigen Pipeline für autonome Geländefahrzeuge kombiniert: Low-Level-, Mid-Level- und High-Level-Perzeption. Die vorgeschlagenen klassischen Methoden und maschinellen Lernmethoden (ML) zur Perzeption bzw.~Wahrnehmung ergänzen sich gegenseitig. Darüber hinaus ermöglicht die Kombination von Perzeptions- und Validierungsmethoden für jede Ebene eine zuverlässige Wahrnehmung der möglicherweise unbekannten Umgebung, wobei lose und eng gekoppelte Validierungsmethoden kombiniert werden, um eine ausreichende, aber flexible Bewertung der vorgeschlagenen Perzeptionsmethoden zu gewährleisten. Alle Methoden wurden als einzelne Module innerhalb der in dieser Arbeit vorgeschlagenen Perzeptions- und Validierungspipeline entwickelt, und ihre flexible Kombination ermöglicht verschiedene Pipelinedesigns für eine Vielzahl von Geländefahrzeugen und Anwendungsfällen je nach Bedarf. Low-Level-Perzeption gewährleistet eine eng gekoppelte Konfidenzbewertung für rohe 2D- und 3D-Sensordaten, um Sensorausfälle zu erkennen und eine ausreichende Genauigkeit der Sensordaten zu gewährleisten. Darüber hinaus werden neuartige Kalibrierungs- und Registrierungsansätze für Multisensorsysteme in der Perzeption vorgestellt, welche lediglich die Struktur der Umgebung nutzen, um die erfassten Sensordaten zu registrieren: ein halbautomatischer Registrierungsansatz zur Registrierung mehrerer 3D~Light Detection and Ranging (LiDAR) Sensoren und ein vertrauensbasiertes Framework, welches verschiedene Registrierungsmethoden kombiniert und die Registrierung verschiedener Sensoren mit unterschiedlichen Messprinzipien ermöglicht. Dabei validiert die Kombination mehrerer Registrierungsmethoden die Registrierungsergebnisse in einer eng gekoppelten Weise. Mid-Level-Perzeption ermöglicht die 3D-Rekonstruktion unstrukturierter Umgebungen mit zwei Verfahren zur Schätzung der Disparität von Stereobildern: ein klassisches, korrelationsbasiertes Verfahren für Hyperspektralbilder, welches eine begrenzte Menge an Test- und Validierungsdaten erfordert, und ein zweites Verfahren, welches die Disparität aus Graustufenbildern mit neuronalen Faltungsnetzen (CNNs) schätzt. Neuartige Disparitätsfehlermetriken und eine Evaluierungs-Toolbox für die 3D-Rekonstruktion von Stereobildern ergänzen die vorgeschlagenen Methoden zur Disparitätsschätzung aus Stereobildern und ermöglichen deren lose gekoppelte Validierung. High-Level-Perzeption konzentriert sich auf die Interpretation von einzelnen 3D-Punktwolken zur Befahrbarkeitsanalyse, Objekterkennung und Hindernisvermeidung. Eine Domänentransferanalyse für State-of-the-art-Methoden zur semantischen 3D-Segmentierung liefert Empfehlungen für eine möglichst exakte Segmentierung in neuen Zieldomänen ohne eine Generierung neuer Trainingsdaten. Der vorgestellte Trainingsansatz für 3D-Segmentierungsverfahren mit CNNs kann die benötigte Menge an Trainingsdaten weiter reduzieren. Methoden zur Erklärbarkeit künstlicher Intelligenz vor und nach der Modellierung ermöglichen eine lose gekoppelte Validierung der vorgeschlagenen High-Level-Methoden mit Datensatzbewertung und modellunabhängigen Erklärungen für CNN-Vorhersagen. Altlastensanierung und Militärlogistik sind die beiden Hauptanwendungsfälle in unstrukturierten Umgebungen, welche in dieser Arbeit behandelt werden. Diese Anwendungsszenarien zeigen auch, wie die Lücke zwischen der Entwicklung einzelner Methoden und ihrer Integration in die Verarbeitungskette für autonome Geländefahrzeuge mit Lokalisierung, Kartierung, Planung und Steuerung geschlossen werden kann. Zusammenfassend lässt sich sagen, dass die vorgeschlagene Pipeline flexible Perzeptionslösungen für autonome Geländefahrzeuge bietet und die begleitende Validierung eine exakte und vertrauenswürdige Perzeption unstrukturierter Umgebungen gewährleistet

Visual inertial SLAM dense mapping for indoor autonomous navigation

openIndoor navigation is a really important topic for many robotics application. The vast majority of tasks that a robot can complete in an indoor environment depends on the fact that the robot is able to orient itself inside it. SLAM algorithms give this ability to the robot but their performance strongly depends on the sensors on which the system is built. This thesis will focus on building a comprehensive starting guide on visual inertial SLAM (in particular ORB-SLAM3), which use only monocular camera to recover camera position and build a map of the surroundings, and accelerometer data to recover real world scale. In this work ORB-SLAM3 will be integrated with a relative depth estimation neural network to build a dense map of the environment rather than a sparse one. Dense map are in fact really fundamental for indoor navigation

Robust dense visual SLAM using sensor fusion and motion segmentation

Visual simultaneous localisation and mapping (SLAM) is an important technique for enabling mobile robots to navigate autonomously within their environments. Using cameras, robots reconstruct a representation of their environment and simultaneously localise themselves within it. A dense visual SLAM system produces a high-resolution and detailed reconstruction of the environment which can be used for obstacle avoidance or semantic reasoning. State-of-the-art dense visual SLAM systems demonstrate robust performance and impressive accuracy in ideal conditions. However, these techniques are based on requirements which limit the extent to which they can be deployed in real applications. Fundamentally, they require constant scene illumination, smooth camera motion and no moving objects being present in the scene. Overcoming these requirements is not trivial and significant effort is needed to make dense visual SLAM approaches more robust to real-world conditions. The objective of this thesis is to develop dense visual SLAM systems which are more robust to real-world visually challenging conditions. For this, we leverage sensor fusion and motion segmentation for situations where camera data is unsuitable. The first contribution is a visual SLAM system for the NASA Valkyrie humanoid robot which is robust to the robot’s operation. It is based on a sensor fusion approach which combines visual SLAM and leg odometry to demonstrate increased robustness to illumination changes and fast camera motion. Second, we research methods for robust visual odometry in the presence of moving objects. We propose a formulation for joint visual odometry and motion segmentation that demonstrates increased robustness in scenes with moving objects compared to state-of-the-art approaches. We then extend this method using inertial information from a gyroscope to compare the contributions of motion segmentation and motion prior integration for robustness to scene dynamics. As part of this study we provide a dataset recorded in scenes with different numbers of moving objects. In conclusion, we find that both motion segmentation and motion prior integration are necessary for achieving significantly better results in real-world conditions. While motion priors increase robustness, motion segmentation increases the accuracy of the reconstruction results through filtering of moving objects.Edinburgh Centre for RoboticsEngineering and Physical Sciences Research Council (EPSRC

Contributions to improve the technologies supporting unmanned aircraft operations

Mención Internacional en el título de doctorUnmanned Aerial Vehicles (UAVs), in their smaller versions known as drones, are becoming increasingly important in today's societies. The systems that make them up present a multitude of challenges, of which error can be considered the common denominator. The perception of the environment is measured by sensors that have errors, the models that interpret the information and/or define behaviors are approximations of the world and therefore also have errors. Explaining error allows extending the limits of deterministic models to address real-world problems. The performance of the technologies embedded in drones depends on our ability to understand, model, and control the error of the systems that integrate them, as well as new technologies that may emerge. Flight controllers integrate various subsystems that are generally dependent on other systems. One example is the guidance systems. These systems provide the engine's propulsion controller with the necessary information to accomplish a desired mission. For this purpose, the flight controller is made up of a control law for the guidance system that reacts to the information perceived by the perception and navigation systems. The error of any of the subsystems propagates through the ecosystem of the controller, so the study of each of them is essential. On the other hand, among the strategies for error control are state-space estimators, where the Kalman filter has been a great ally of engineers since its appearance in the 1960s. Kalman filters are at the heart of information fusion systems, minimizing the error covariance of the system and allowing the measured states to be filtered and estimated in the absence of observations. State Space Models (SSM) are developed based on a set of hypotheses for modeling the world. Among the assumptions are that the models of the world must be linear, Markovian, and that the error of their models must be Gaussian. In general, systems are not linear, so linearization are performed on models that are already approximations of the world. In other cases, the noise to be controlled is not Gaussian, but it is approximated to that distribution in order to be able to deal with it. On the other hand, many systems are not Markovian, i.e., their states do not depend only on the previous state, but there are other dependencies that state space models cannot handle. This thesis deals a collection of studies in which error is formulated and reduced. First, the error in a computer vision-based precision landing system is studied, then estimation and filtering problems from the deep learning approach are addressed. Finally, classification concepts with deep learning over trajectories are studied. The first case of the collection xviiistudies the consequences of error propagation in a machine vision-based precision landing system. This paper proposes a set of strategies to reduce the impact on the guidance system, and ultimately reduce the error. The next two studies approach the estimation and filtering problem from the deep learning approach, where error is a function to be minimized by learning. The last case of the collection deals with a trajectory classification problem with real data. This work completes the two main fields in deep learning, regression and classification, where the error is considered as a probability function of class membership.Los vehículos aéreos no tripulados (UAV) en sus versiones de pequeño tamaño conocidos como drones, van tomando protagonismo en las sociedades actuales. Los sistemas que los componen presentan multitud de retos entre los cuales el error se puede considerar como el denominador común. La percepción del entorno se mide mediante sensores que tienen error, los modelos que interpretan la información y/o definen comportamientos son aproximaciones del mundo y por consiguiente también presentan error. Explicar el error permite extender los límites de los modelos deterministas para abordar problemas del mundo real. El rendimiento de las tecnologías embarcadas en los drones, dependen de nuestra capacidad de comprender, modelar y controlar el error de los sistemas que los integran, así como de las nuevas tecnologías que puedan surgir. Los controladores de vuelo integran diferentes subsistemas los cuales generalmente son dependientes de otros sistemas. Un caso de esta situación son los sistemas de guiado. Estos sistemas son los encargados de proporcionar al controlador de los motores información necesaria para cumplir con una misión deseada. Para ello se componen de una ley de control de guiado que reacciona a la información percibida por los sistemas de percepción y navegación. El error de cualquiera de estos sistemas se propaga por el ecosistema del controlador siendo vital su estudio. Por otro lado, entre las estrategias para abordar el control del error se encuentran los estimadores en espacios de estados, donde el filtro de Kalman desde su aparición en los años 60, ha sido y continúa siendo un gran aliado para los ingenieros. Los filtros de Kalman son el corazón de los sistemas de fusión de información, los cuales minimizan la covarianza del error del sistema, permitiendo filtrar los estados medidos y estimarlos cuando no se tienen observaciones. Los modelos de espacios de estados se desarrollan en base a un conjunto de hipótesis para modelar el mundo. Entre las hipótesis se encuentra que los modelos del mundo han de ser lineales, markovianos y que el error de sus modelos ha de ser gaussiano. Generalmente los sistemas no son lineales por lo que se realizan linealizaciones sobre modelos que a su vez ya son aproximaciones del mundo. En otros casos el ruido que se desea controlar no es gaussiano, pero se aproxima a esta distribución para poder abordarlo. Por otro lado, multitud de sistemas no son markovianos, es decir, sus estados no solo dependen del estado anterior, sino que existen otras dependencias que los modelos de espacio de estados no son capaces de abordar. Esta tesis aborda un compendio de estudios sobre los que se formula y reduce el error. En primer lugar, se estudia el error en un sistema de aterrizaje de precisión basado en visión por computador. Después se plantean problemas de estimación y filtrado desde la aproximación del aprendizaje profundo. Por último, se estudian los conceptos de clasificación con aprendizaje profundo sobre trayectorias. El primer caso del compendio estudia las consecuencias de la propagación del error de un sistema de aterrizaje de precisión basado en visión artificial. En este trabajo se propone un conjunto de estrategias para reducir el impacto sobre el sistema de guiado, y en última instancia reducir el error. Los siguientes dos estudios abordan el problema de estimación y filtrado desde la perspectiva del aprendizaje profundo, donde el error es una función que minimizar mediante aprendizaje. El último caso del compendio aborda un problema de clasificación de trayectorias con datos reales. Con este trabajo se completan los dos campos principales en aprendizaje profundo, regresión y clasificación, donde se plantea el error como una función de probabilidad de pertenencia a una clase.I would like to thank the Ministry of Science and Innovation for granting me the funding with reference PRE2018-086793, associated to the project TEC2017-88048-C2-2-R, which provide me the opportunity to carry out all my PhD. activities, including completing an international research internship.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: Antonio Berlanga de Jesús.- Secretario: Daniel Arias Medina.- Vocal: Alejandro Martínez Cav

Cooperative perception for driving applications

An automated vehicle needs to understand its driving environment to operate safely and reliably. This function is performed within the vehicle's perception system, where data from on-board sensors is processed by multiple perception algorithms, including 3D object detection, semantic segmentation and object tracking. To take advantage of different sensor modalities, multiple perception methods fusing the data from on-board cameras and lidars have been devised. However, sensing exclusively from a single vehicle is inherently prone to occlusions and a limited field-of-view that indiscriminately affects all sensor modalities. Alternatively, cooperative perception incorporates sensor observations from multiple view points distributed throughout the driving environment. This research investigates if and how cooperative perception is capable of improving the detection of objects in driving environments using data from multiple, spatially diverse sensors. Over the course of this thesis, four studies are conducted considering different aspects of cooperative perception. The first study considers the various impacts of occlusions and sensor noise on the classification of objects in images and investigates how to fuse data from multiple images. This study serves as a proof-of-concept to validate the core idea of cooperative perception and presents quantitative results on how well cooperative perception can mitigate such impairments. The second study generalises the problem to 3D object detection using infrastructure sensors capable of providing depth information and investigates different sensor fusion approaches for such sensors. Three sensor fusion approaches are devised and evaluated in terms of object detection performance, communication bandwidth and inference time. This study also investigates the impact of the number of sensors in the performance of object detection. The results show that the proposed cooperative 3D object detection method achieves more than thrice the number of correct detections compared to single sensor baselines, while also reducing the number of false positive detections. Next, the problem of optimising the pose of fixed infrastructure sensors in cluttered driving environments is considered. Two novel sensor pose optimisation methods are proposed, one using gradient-based optimisation and one using integer programming techniques, to maximise the visibility of objects. Both use a novel visibility model, based on a rendering engine, capable of determining occlusions between objects. The results suggest that both methods have the potential to guide the cost effective deployment of sensor networks in cooperative perception applications. Finally, the last study considers the problem of estimating the relative pose between non-static sensors relying on sensor data alone. To that end, a novel and computationally efficient point cloud registration method is proposed using a bespoke feature encoder and attention network. Extensive results show that the proposed method is capable of operating in real-time and is more robust for point clouds with low _eld-of-view overlap compared to existing methods

Towards new sensing capabilities for legged locomotion using real-time state estimation with low-cost IMUs

L'estimation en robotique est un sujet important affecté par les compromis entre certains critères majeurs parmi lesquels nous pouvons citer le temps de calcul et la précision. L'importance de ces deux critères dépend de l'application. Si le temps de calcul n'est pas important pour les méthodes hors ligne, il devient critique lorsque l'application doit s'exécuter en temps réel. De même, les exigences de précision dépendent des applications. Les estimateurs EKF sont largement utilisés pour satisfaire les contraintes en temps réel tout en obtenant une estimation avec des précisions acceptables. Les centrales inertielles (Inertial Measurement Unit - IMU) demeurent des capteurs répandus dnas les problèmes d'estimation de trajectoire. Ces capteurs ont par ailleurs la particularité de fournir des données à une fréquence élevée. La principale contribution de cette thèses est une présentation claire de la méthode de préintégration donnant lieu à une meilleure utilisation des centrales inertielles. Nous appliquons cette méthode aux problèmes d'estimation dans les cas de la navigation piétonne et celle des robots humanoïdes. Nous souhaitons par ailleurs montrer que l'estimation en temps réel à l'aide d'une centrale inertielle à faible coût est possible avec des méthodes d'optimisation tout en formulant les problèmes à l'aide d'un modèle graphique bien que ces méthodes soient réputées pour leurs coûts élevés en terme de calculs. Nous étudions également la calibration des centrales inertielles, une étape qui demeure critique pour leurs utilisations. Les travaux réalisés au cours de cette thèse ont été pensés en gardant comme perspective à moyen terme le SLAM visuel-inertiel. De plus, ce travail aborde une autre question concernant les robots à jambes. Contrairement à leur architecture habituelle, pourrions-nous utiliser plusieurs centrales inertielles à faible coût sur le robot pour obtenir des informations précieuses sur le mouvement en cours d'exécution ?Estimation in robotics is an important subject affected by trade-offs between some major critera from which we can cite the computation time and the accuracy. The importance of these two criteria are application-dependent. If the computation time is not important for off-line methods, it becomes critical when the application has to run on real-time. Similarly, accuracy requirements are dependant on the applications. EKF estimators are widely used to satisfy real-time constraints while achieving acceptable accuracies. One sensor widely used in trajectory estimation problems remains the inertial measurement units (IMUs) providing data at a high rate. The main contribution of this thesis is a clear presentation of the preintegration theory yielding in a better use IMUs. We apply this method for estimation problems in both pedestrian and humanoid robots navigation to show that real-time estimation using a low- cost IMU is possible with smoothing methods while formulating the problems with a factor graph. We also investigate the calibration of the IMUs as it is a critical part of those sensors. All the development made during this thesis was thought with a visual-inertial SLAM background as a mid-term perspective. Firthermore, this work tries to rise another question when it comes to legged robots. In opposition to their usual architecture, could we use multiple low- cost IMUs on the robot to get valuable information about the motion being executed

Robust On-Manifold Optimization for Uncooperative Space Relative Navigation with a Single Camera

Optical cameras are gaining popularity as the suitable sensor for relative navigation in space due to their attractive sizing, power, and cost properties when compared with conventional flight hardware or costly laser-based systems. However, a camera cannot infer depth information on its own, which is often solved by introducing complementary sensors or a second camera. In this paper, an innovative model-based approach is demonstrated to estimate the six-dimensional pose of a target relative to the chaser spacecraft using solely a monocular setup. The observed facet of the target is tackled as a classification problem, where the three-dimensional shape is learned offline using Gaussian mixture modeling. The estimate is refined by minimizing two different robust loss functions based on local feature correspondences. The resulting pseudomeasurements are processed and fused with an extended Kalman filter. The entire optimization framework is designed to operate directly on the SE(3) manifold, uncoupling the process and measurement models from the global attitude state representation. It is validated on realistic synthetic and laboratory datasets of a rendezvous trajectory with the complex spacecraft Envisat, demonstrating estimation of the relative pose with high accuracy over full tumbling motion. Further evaluation is performed on the open-source SPEED dataset