Visual servoing of mobile robots using non-central catadioptric cameras

This paper presents novel contributions on image-based control of a mobile robot using a general catadioptric camera model. A catadioptric camera is usually made up by a combination of a conventional camera and a curved mirror resulting in an omnidirectional sensor capable of providing 360° panoramic views of a scene. Modeling such cameras has been the subject of significant research interest in the computer vision community leading to a deeper understanding of the image properties and also to different models for different types of configurations. Visual servoing applications using catadioptric cameras have essentially been using central cameras and the corresponding unified projection model. So far only in a few cases more general models have been used. In this paper we address the problem of visual servoing using the so-called radial model. The radial model can be applied to many camera configurations and in particular to non-central catadioptric systems with mirrors that are symmetric around an axis coinciding with the optical axis. In this case, we show that the radial model can be used with a non-central catadioptric camera to allow effective image-based visual servoing (IBVS) of a mobile robot. Using this model, which is valid for a large set of catadioptric cameras (central or non-central), new visual features are proposed to control the degrees of freedom of a mobile robot moving on a plane. In addition to several simulation results, a set of experiments was carried out on Robot Operating System (ROS)-based platform which validates the applicability, effectiveness and robustness of the proposed method for image-based control of a non-holonomic robot

Internationales Kolloquium über Anwendungen der Informatik und Mathematik in Architektur und Bauwesen : 20. bis 22.7. 2015, Bauhaus-Universität Weimar

The 20th International Conference on the Applications of Computer Science and Mathematics in Architecture and Civil Engineering will be held at the Bauhaus University Weimar from 20th till 22nd July 2015. Architects, computer scientists, mathematicians, and engineers from all over the world will meet in Weimar for an interdisciplinary exchange of experiences, to report on their results in research, development and practice and to discuss. The conference covers a broad range of research areas: numerical analysis, function theoretic methods, partial differential equations, continuum mechanics, engineering applications, coupled problems, computer sciences, and related topics. Several plenary lectures in aforementioned areas will take place during the conference. We invite architects, engineers, designers, computer scientists, mathematicians, planners, project managers, and software developers from business, science and research to participate in the conference

Visual Odometry and Sparse Scene Reconstruction for UAVs with a Multi-Fisheye Camera System

Autonomously operating UAVs demand a fast localization for navigation, to actively explore unknown areas and to create maps. For pose estimation, many UAV systems make use of a combination of GPS receivers and inertial sensor units (IMU). However, GPS signal coverage may go down occasionally, especially in the close vicinity of objects, and precise IMUs are too heavy to be carried by lightweight UAVs. This and the high cost of high quality IMU motivate the use of inexpensive vision based sensors for localization using visual odometry or visual SLAM (simultaneous localization and mapping) techniques. The first contribution of this thesis is a more general approach to bundle adjustment with an extended version of the projective coplanarity equation which enables us to make use of omnidirectional multi-camera systems which may consist of fisheye cameras that can capture a large field of view with one shot. We use ray directions as observations instead of image points which is why our approach does not rely on a specific projection model assuming a central projection. In addition, our approach allows the integration and estimation of points at infinity, which classical bundle adjustments are not capable of. We show that the integration of far or infinitely far points stabilizes the estimation of the rotation angles of the camera poses. In its second contribution, we employ this approach to bundle adjustment in a highly integrated system for incremental pose estimation and mapping on light-weight UAVs. Based on the image sequences of a multi-camera system our system makes use of tracked feature points to incrementally build a sparse map and incrementally refines this map using the iSAM2 algorithm. Our system is able to optionally integrate GPS information on the level of carrier phase observations even in underconstrained situations, e.g. if only two satellites are visible, for georeferenced pose estimation. This way, we are able to use all available information in underconstrained GPS situations to keep the mapped 3D model accurate and georeferenced. In its third contribution, we present an approach for re-using existing methods for dense stereo matching with fisheye cameras, which has the advantage that highly optimized existing methods can be applied as a black-box without modifications even with cameras that have field of view of more than 180 deg. We provide a detailed accuracy analysis of the obtained dense stereo results. The accuracy analysis shows the growing uncertainty of observed image points of fisheye cameras due to increasing blur towards the image border. Core of the contribution is a rigorous variance component estimation which allows to estimate the variance of the observed disparities at an image point as a function of the distance of that point to the principal point. We show that this improved stochastic model provides a more realistic prediction of the uncertainty of the triangulated 3D points.Autonom operierende UAVs benötigen eine schnelle Lokalisierung zur Navigation, zur Exploration unbekannter Umgebungen und zur Kartierung. Zur Posenbestimmung verwenden viele UAV-Systeme eine Kombination aus GPS-Empfängern und Inertial-Messeinheiten (IMU). Die Verfügbarkeit von GPS-Signalen ist jedoch nicht überall gewährleistet, insbesondere in der Nähe abschattender Objekte, und präzise IMUs sind für leichtgewichtige UAVs zu schwer. Auch die hohen Kosten qualitativ hochwertiger IMUs motivieren den Einsatz von kostengünstigen bildgebenden Sensoren zur Lokalisierung mittels visueller Odometrie oder SLAM-Techniken zur simultanen Lokalisierung und Kartierung. Im ersten wissenschaftlichen Beitrag dieser Arbeit entwickeln wir einen allgemeineren Ansatz für die Bündelausgleichung mit einem erweiterten Modell für die projektive Kollinearitätsgleichung, sodass auch omnidirektionale Multikamerasysteme verwendet werden können, welche beispielsweise bestehend aus Fisheyekameras mit einer Aufnahme einen großen Sichtbereich abdecken. Durch die Integration von Strahlrichtungen als Beobachtungen ist unser Ansatz nicht von einem kameraspezifischen Abbildungsmodell abhängig solange dieses der Zentralprojektion folgt. Zudem erlaubt unser Ansatz die Integration und Schätzung von unendlich fernen Punkten, was bei klassischen Bündelausgleichungen nicht möglich ist. Wir zeigen, dass durch die Integration weit entfernter und unendlich ferner Punkte die Schätzung der Rotationswinkel der Kameraposen stabilisiert werden kann. Im zweiten Beitrag verwenden wir diesen entwickelten Ansatz zur Bündelausgleichung für ein System zur inkrementellen Posenschätzung und dünnbesetzten Kartierung auf einem leichtgewichtigen UAV. Basierend auf den Bildsequenzen eines Mulitkamerasystems baut unser System mittels verfolgter markanter Bildpunkte inkrementell eine dünnbesetzte Karte auf und verfeinert diese inkrementell mittels des iSAM2-Algorithmus. Unser System ist in der Lage optional auch GPS Informationen auf dem Level von GPS-Trägerphasen zu integrieren, wodurch sogar in unterbestimmten Situation - beispielsweise bei nur zwei verfügbaren Satelliten - diese Informationen zur georeferenzierten Posenschätzung verwendet werden können. Im dritten Beitrag stellen wir einen Ansatz zur Verwendung existierender Methoden für dichtes Stereomatching mit Fisheyekameras vor, sodass hoch optimierte existierende Methoden als Black Box ohne Modifzierungen sogar mit Kameras mit einem Gesichtsfeld von mehr als 180 Grad verwendet werden können. Wir stellen eine detaillierte Genauigkeitsanalyse basierend auf dem Ergebnis des dichten Stereomatchings dar. Die Genauigkeitsanalyse zeigt, wie stark die Genauigkeit beobachteter Bildpunkte bei Fisheyekameras zum Bildrand aufgrund von zunehmender Unschärfe abnimmt. Das Kernstück dieses Beitrags ist eine Varianzkomponentenschätzung, welche die Schätzung der Varianz der beobachteten Disparitäten an einem Bildpunkt als Funktion von der Distanz dieses Punktes zum Hauptpunkt des Bildes ermöglicht. Wir zeigen, dass dieses verbesserte stochastische Modell eine realistischere Prädiktion der Genauigkeiten der 3D Punkte ermöglicht

Internationales Kolloquium über Anwendungen der Informatik und Mathematik in Architektur und Bauwesen : 20. bis 22.7. 2015, Bauhaus-Universität Weimar

The 20th International Conference on the Applications of Computer Science and Mathematics in Architecture and Civil Engineering will be held at the Bauhaus University Weimar from 20th till 22nd July 2015. Architects, computer scientists, mathematicians, and engineers from all over the world will meet in Weimar for an interdisciplinary exchange of experiences, to report on their results in research, development and practice and to discuss. The conference covers a broad range of research areas: numerical analysis, function theoretic methods, partial differential equations, continuum mechanics, engineering applications, coupled problems, computer sciences, and related topics. Several plenary lectures in aforementioned areas will take place during the conference. We invite architects, engineers, designers, computer scientists, mathematicians, planners, project managers, and software developers from business, science and research to participate in the conference

Robust navigation for industrial service robots

Pla de Doctorats Industrials de la Generalitat de CatalunyaRobust, reliable and safe navigation is one of the fundamental problems of robotics. Throughout the present thesis, we tackle the problem of navigation for robotic industrial mobile-bases. We identify its components and analyze their respective challenges in order to address them. The research work presented here ultimately aims at improving the overall quality of the navigation stack of a commercially available industrial mobile-base. To introduce and survey the overall problem we first break down the navigation framework into clearly identified smaller problems. We examine the Simultaneous Localization and Mapping (SLAM) problem, recalling its mathematical grounding and exploring the state of the art. We then review the problem of planning the trajectory of a mobile-base toward a desired goal in the generated environment representation. Finally we investigate and clarify the use of the subset of the Lie theory that is useful in robotics. The first problem tackled is the recognition of place for closing loops in SLAM. Loop closure refers to the ability of a robot to recognize a previously visited location and infer geometrical information between its current and past locations. Using only a 2D laser range finder sensor, we address the problem using a technique borrowed from the field of Natural Language Processing (NLP) which has been successfully applied to image-based place recognition, namely the Bag-of-Words. We further improve the method with two proposals inspired from NLP. Firstly, the comparison of places is strengthened by considering the natural relative order of features in each individual sensor reading. Secondly, topological correspondences between places in a corpus of visited places are established in order to promote together instances that are ‘close’ to one another. We then tackle the problem of motion model calibration for odometry estimation. Given a mobile-base embedding an exteroceptive sensor able to observe ego-motion, we propose a novel formulation for estimating the intrinsic parameters of an odometry motion model. Resorting to an adaptation of the pre-integration theory initially developed for inertial motion sensors, we employ iterative nonlinear on-manifold optimization to estimate the wheel radii and wheel separation. The method is further extended to jointly estimate both the intrinsic parameters of the odometry model together with the extrinsic parameters of the embedded sensor. The method is shown to accommodate to variation in model parameters quickly when the vehicle is subject to physical changes during operation. Following the generation of a map in which the robot is localized, we address the problem of estimating trajectories for motion planning. We devise a new method for estimating a sequence of robot poses forming a smooth trajectory. Regardless of the Lie group considered, the trajectory is seen as a collection of states lying on a spline with non-vanishing n-th derivatives at each point. Formulated as a multi-objective nonlinear optimization problem, it allows for the addition of cost functions such as velocity and acceleration limits, collision avoidance and more. The proposed method is evaluated for two different motion planning tasks, the planning of trajectories for a mobile-base evolving in the SE(2) manifold, and the planning of the motion of a multi-link robotic arm whose end-effector evolves in the SE(3) manifold. From our study of Lie theory, we developed a new, ready to use, programming library called `manif’. The library is open source, publicly available and is developed following good software programming practices. It is designed so that it is easy to integrate and manipulate, and allows for flexible use while facilitating the possibility to extend it beyond the already implemented Lie groups.La navegación autónoma es uno de los problemas fundamentales de la robótica, y sus diferentes desafíos se han estudiado durante décadas. El desarrollo de métodos de navegación robusta, confiable y segura es un factor clave para la creación de funcionalidades de nivel superior en robots diseñados para operar en entornos con humanos. A lo largo de la presente tesis, abordamos el problema de navegación para bases robóticas móviles industriales; identificamos los elementos de un sistema de navegación; y analizamos y tratamos sus desafíos. El trabajo de investigación presentado aquí tiene como último objetivo mejorar la calidad general del sistema completo de navegación de una base móvil industrial disponible comercialmente. Para estudiar el problema de navegación, primero lo desglosamos en problemas menores claramente identificados. Examinamos el subproblema de mapeo del entorno y localización del robot simultáneamente (SLAM por sus siglas en ingles) y estudiamos el estado del arte del mismo. Al hacerlo, recordamos y detallamos la base matemática del problema de SLAM. Luego revisamos el subproblema de planificación de trayectorias hacia una meta deseada en la representación del entorno generada. Además, como una herramienta para las soluciones que se presentarán más adelante en el desarrollo de la tesis, investigamos y aclaramos el uso de teoría de Lie, centrándonos en el subconjunto de la teoría que es útil para la estimación de estados en robótica. Como primer elemento identificado para mejoras, abordamos el problema de reconocimiento de lugares para cerrar lazos en SLAM. El cierre de lazos se refiere a la capacidad de un robot para reconocer una ubicación visitada previamente e inferí información geométrica entre la ubicación actual del robot y aquellas reconocidas. Usando solo un sensor láser 2D, la tarea es desafiante ya que la percepción del entorno que proporciona el sensor es escasa y limitada. Abordamos el problema utilizando 'bolsas de palabras', una técnica prestada del campo de procesamiento del lenguaje natural (NLP) que se ha aplicado con éxito anteriormente al reconocimiento de lugares basado en imágenes. Nuestro método incluye dos nuevas propuestas inspiradas también en NLP. Primero, la comparación entre lugares candidatos se fortalece teniendo en cuenta el orden relativo natural de las características en cada lectura individual del sensor; y segundo, se establece un corpus de lugares visitados para promover juntos instancias que están "cerca" la una de la otra desde un punto de vista topológico. Evaluamos nuestras propuestas por separado y conjuntamente en varios conjuntos de datos, con y sin ruido, demostrando mejora en la detección de cierres de lazo para sensores láser 2D, con respecto al estado del arte. Luego abordamos el problema de la calibración del modelo de movimiento para la estimación de la edometría. Dado que nuestra base móvil incluye un sensor exteroceptivo capaz de observar el movimiento de la plataforma, proponemos una nueva formulación que permite estimar los parámetros intrínsecos del modelo cinemático de la plataforma durante el cómputo de la edometría del vehículo. Hemos recurrido a una adaptación de la teoría de reintegración inicialmente desarrollado para unidades inerciales de medida, y aplicado la técnica a nuestro modelo cinemático. El método nos permite, mediante optimización iterativa no lineal, la estimación del valor del radio de las ruedas de forma independiente y de la separación entre las mismas. El método se amplía posteriormente par idéntica de forma simultánea, estos parámetros intrínsecos junto con los parámetros extrínsecos que ubican el sensor láser con respecto al sistema de referencia de la base móvil. El método se valida en simulación y en un entorno real y se muestra que converge hacia los verdaderos valores de los parámetros. El método permite la adaptación de los parámetros intrínsecos del modelo cinemático de la plataforma derivados de cambios físicos durante la operación, tales como el impacto que el cambio de carga sobre la plataforma tiene sobre el diámetro de las ruedas. Como tercer subproblema de navegación, abordamos el reto de planificar trayectorias de movimiento de forma suave. Desarrollamos un método para planificar la trayectoria como una secuencia de configuraciones sobre una spline con n-ésimas derivadas en todos los puntos, independientemente del grupo de Lie considerado. Al ser formulado como un problema de optimización no lineal con múltiples objetivos, es posible agregar funciones de coste al problema de optimización que permitan añadir límites de velocidad o aceleración, evasión de colisiones, etc. El método propuesto es evaluado en dos tareas de planificación de movimiento diferentes, la planificación de trayectorias para una base móvil que evoluciona en la variedad SE(2), y la planificación del movimiento de un brazo robótico cuyo efector final evoluciona en la variedad SE(3). Además, cada tarea se evalúa en escenarios con complejidad de forma incremental, y se muestra un rendimiento comparable o mejor que el estado del arte mientras produce resultados más consistentes. Desde nuestro estudio de la teoría de Lie, desarrollamos una nueva biblioteca de programación llamada “manif”. La biblioteca es de código abierto, está disponible públicamente y se desarrolla siguiendo las buenas prácticas de programación de software. Esta diseñado para que sea fácil de integrar y manipular, y permite flexibilidad de uso mientras se facilita la posibilidad de extenderla más allá de los grupos de Lie inicialmente implementados. Además, la biblioteca se muestra eficiente en comparación con otras soluciones existentes. Por fin, llegamos a la conclusión del estudio de doctorado. Examinamos el trabajo de investigación y trazamos líneas para futuras investigaciones. También echamos un vistazo en los últimos años y compartimos una visión personal y experiencia del desarrollo de un doctorado industrial.Postprint (published version

Multi-camera simultaneous localization and mapping

In this thesis, we study two aspects of simultaneous localization and mapping (SLAM) for multi-camera systems: minimal solution methods for the scaled motion of non-overlapping and partially overlapping two camera systems and enabling online, real-time mapping of large areas using the parallelism inherent in the visual simultaneous localization and mapping (VSLAM) problem. We present the only existing minimal solution method for six degree of freedom structure and motion estimation using a non-overlapping, rigid two camera system with known intrinsic and extrinsic calibration. One example application of our method is the three-dimensional reconstruction of urban scenes from video. Because our method does not require the cameras' fields-of-view to overlap, we are able to maximize coverage of the scene and avoid processing redundant, overlapping imagery. Additionally, we developed a minimal solution method for partially overlapping stereo camera systems to overcome degeneracies inherent to non-overlapping two-camera systems but still have a wide total field of view. The method takes two stereo images as its input. It uses one feature visible in all four views and three features visible across two temporal view pairs to constrain the system camera's motion. We show in synthetic experiments that our method creates rotation and translation estimates that are more accurate than the perspective three-point method as the overlap in the stereo camera's fields-of-view is reduced. A final part of this thesis is the development of an online, real-time visual SLAM system that achieves real-time speed by exploiting the parallelism inherent in the VSLAM problem. We show that feature tracking, relative pose estimation, and global mapping operations such as loop detection and loop correction can be effectively parallelized. Additionally, we demonstrate that a combination of short baseline, differentially tracked corner features, which can be tracked at high frame rates and wide baseline matchable but slower to compute features such as the scale-invariant feature transform can facilitate high speed visual odometry and at the same time support location recognition for loop detection and global geometric error correction

Distributed scene reconstruction from multiple mobile platforms

Recent research on mobile robotics has produced new designs that provide house-hold robots with omnidirectional motion. The image sensor embedded in these devices motivates the application of 3D vision techniques on them for navigation and mapping purposes. In addition to this, distributed cheapsensing systems acting as unitary entity have recently been discovered as an efficient alternative to expensive mobile equipment. In this work we present an implementation of a visual reconstruction method, structure from motion (SfM), on a low-budget, omnidirectional mobile platform, and extend this method to distributed 3D scene reconstruction with several instances of such a platform. Our approach overcomes the challenges yielded by the plaform. The unprecedented levels of noise produced by the image compression typical of the platform is processed by our feature filtering methods, which ensure suitable feature matching populations for epipolar geometry estimation by means of a strict quality-based feature selection. The robust pose estimation algorithms implemented, along with a novel feature tracking system, enable our incremental SfM approach to novelly deal with ill-conditioned inter-image configurations provoked by the omnidirectional motion. The feature tracking system developed efficiently manages the feature scarcity produced by noise and outputs quality feature tracks, which allow robust 3D mapping of a given scene even if - due to noise - their length is shorter than what it is usually assumed for performing stable 3D reconstructions. The distributed reconstruction from multiple instances of SfM is attained by applying loop-closing techniques. Our multiple reconstruction system merges individual 3D structures and resolves the global scale problem with minimal overlaps, whereas in the literature 3D mapping is obtained by overlapping stretches of sequences. The performance of this system is demonstrated in the 2-session case. The management of noise, the stability against ill-configurations and the robustness of our SfM system is validated on a number of experiments and compared with state-of-the-art approaches. Possible future research areas are also discussed

Medical SLAM in an autonomous robotic system

One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-operative morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilities by observing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted instruments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This thesis addresses the ambitious goal of achieving surgical autonomy, through the study of the anatomical environment by Initially studying the technology present and what is needed to analyze the scene: vision sensors. A novel endoscope for autonomous surgical task execution is presented in the first part of this thesis. Which combines a standard stereo camera with a depth sensor. This solution introduces several key advantages, such as the possibility of reconstructing the 3D at a greater distance than traditional endoscopes. Then the problem of hand-eye calibration is tackled, which unites the vision system and the robot in a single reference system. Increasing the accuracy in the surgical work plan. In the second part of the thesis the problem of the 3D reconstruction and the algorithms currently in use were addressed. In MIS, simultaneous localization and mapping (SLAM) can be used to localize the pose of the endoscopic camera and build ta 3D model of the tissue surface. Another key element for MIS is to have real-time knowledge of the pose of surgical tools with respect to the surgical camera and underlying anatomy. Starting from the ORB-SLAM algorithm we have modified the architecture to make it usable in an anatomical environment by adding the registration of the pre-operative information of the intervention to the map obtained from the SLAM. Once it has been proven that the slam algorithm is usable in an anatomical environment, it has been improved by adding semantic segmentation to be able to distinguish dynamic features from static ones. All the results in this thesis are validated on training setups, which mimics some of the challenges of real surgery and on setups that simulate the human body within Autonomous Robotic Surgery (ARS) and Smart Autonomous Robotic Assistant Surgeon (SARAS) projects

Medical SLAM in an autonomous robotic system

One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-operative morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilities by observing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted instruments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This thesis addresses the ambitious goal of achieving surgical autonomy, through the study of the anatomical environment by Initially studying the technology present and what is needed to analyze the scene: vision sensors. A novel endoscope for autonomous surgical task execution is presented in the first part of this thesis. Which combines a standard stereo camera with a depth sensor. This solution introduces several key advantages, such as the possibility of reconstructing the 3D at a greater distance than traditional endoscopes. Then the problem of hand-eye calibration is tackled, which unites the vision system and the robot in a single reference system. Increasing the accuracy in the surgical work plan. In the second part of the thesis the problem of the 3D reconstruction and the algorithms currently in use were addressed. In MIS, simultaneous localization and mapping (SLAM) can be used to localize the pose of the endoscopic camera and build ta 3D model of the tissue surface. Another key element for MIS is to have real-time knowledge of the pose of surgical tools with respect to the surgical camera and underlying anatomy. Starting from the ORB-SLAM algorithm we have modified the architecture to make it usable in an anatomical environment by adding the registration of the pre-operative information of the intervention to the map obtained from the SLAM. Once it has been proven that the slam algorithm is usable in an anatomical environment, it has been improved by adding semantic segmentation to be able to distinguish dynamic features from static ones. All the results in this thesis are validated on training setups, which mimics some of the challenges of real surgery and on setups that simulate the human body within Autonomous Robotic Surgery (ARS) and Smart Autonomous Robotic Assistant Surgeon (SARAS) projects