Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms. The treatment is centered on robot motion planning but integrates material on planning in discrete spaces. A major part of the book is devoted to planning under uncertainty, including decision theory, Markov decision processes, and information spaces, which are the “configuration spaces” of all sensor-based planning problems. The last part of the book delves into planning under differential constraints that arise when automating the motions of virtually any mechanical system. Developed from courses taught by the author, the book is intended for students, engineers, and researchers in robotics, artificial intelligence, and control theory as well as computer graphics, algorithms, and computational biology.

Planning Algorithms: Introductory Material

Humans perceive the three-dimensional structure of the world with apparent ease. However, despite all of the recent advances in computer vision research, the dream of having a computer interpret an image at the same level as a two-year old remains elusive. Why is computer vision such a challenging problem and what is the current state of the art? Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos. More than just a source of recipes, this exceptionally authoritative and comprehensive textbook/reference also takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene. These problems are also analyzed using statistical models and solved using rigorous engineering techniques Topics and features: structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses; presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects; provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, and Bayesian estimation theory; suggests additional reading at the end of each chapter, including the latest research in each sub-field, in addition to a full Bibliography at the end of the book; supplies supplementary course material for students at the associated website, http://szeliski.org/Book/. Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision.

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Computer Vision: Algorithms and Applications

We present a real-time algorithm which can recover the 3D trajectory of a monocular camera, moving rapidly through a previously unknown scene. Our system, which we dub MonoSLAM, is the first successful application of the SLAM methodology from mobile robotics to the "pure vision" domain of a single uncontrolled camera, achieving real time but drift-free performance inaccessible to structure from motion approaches. The core of the approach is the online creation of a sparse but persistent map of natural landmarks within a probabilistic framework. Our key novel contributions include an active approach to mapping and measurement, the use of a general motion model for smooth camera movement, and solutions for monocular feature initialization and feature orientation estimation. Together, these add up to an extremely efficient and robust algorithm which runs at 30 Hz with standard PC and camera hardware. This work extends the range of robotic systems in which SLAM can be usefully applied, but also opens up new areas. We present applications of MonoSLAM to real-time 3D localization and mapping for a high-performance full-size humanoid robot and live augmented reality with a hand-held camera

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MonoSLAM: Real-Time Single Camera SLAM

This paper describes the simultaneous localization and mapping (SLAM) problem and the essential methods for solving the SLAM problem and summarizes key implementations and demonstrations of the method. While there are still many practical issues to overcome, especially in more complex outdoor environments, the general SLAM method is now a well understood and established part of robotics. Another part of the tutorial summarized more recent works in addressing some of the remaining issues in SLAM, including computation, feature representation, and data association

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Simultaneous localization and mapping: part I

This paper presents an application of tool insertion using model based vision. The requisite technologies, including model representation, sensor calibration and error quantification are discussed. These techniques are applied to a nuclear servicing task in a physical mock-up of a steam generator. 1. INTRODUCTION Tool insertion is a class of problems that mate two noncompliant parts in the presence of geometric uncertainty. For a subclass of tool insertion, an accurate model of the tool, its mating receptacle, and its surrounding environment contains sufficient information to drive the task. For these tool insertion problems a model does exist, but is not always accurate ,>nough to enable reliable manipulation. Model based vision offers one solution to the problem of reducing model uncertainty and providing precise scene data. Using sensors, an approximate model is corrected until it is SutXciently accurate for manipulation. Achieving model accuracy requires the application of a number of techniques such as sensor calibration, sensor error estimation and the identification of spurious sensor data. In this paper, model based vision is shown to be a viable solution to a tool insertion problem for the nuclear servicing industry. The development of a complete model based tool insertion system including sensor calibration, image processing, error estimation and methods for motion planning in the presence of sensor dropout is discussed. 2. PRIORWORK The development of manipulation for model based tool insertion requires that vision systems for model correction be developed, and that the manipulator be driven based on model information. Much work has been done in robotics toward the development of automated manipulation systems, the references cited here are certainly not all inclusive, but offer an overview of the technologies that influenced our work. Mitchell

https://ieeexplore.ieee.org/iel2/589/4210/00161142.pdf

An application of tool insertion using model based vision

Humans are able to pump gas into a car with little or no difficulty. This task is characterized by two sources of force: that from the nozzle contacting the car and that from the hose attached to the pump. The task succeeds due to the appreciable skill of a human and a forgiveness in the connection. The robotic mating of connectors burdened by forces from sources like the gas hose is beyond the current state of art. The research presented in this dissertation develops technology for robots to mate connectors that concurrently experience appreciable forces from encumbrances, like those from hoses, cables and oscillating masses, in addition to forces from contact. 
Effective force guided assembly under the influence of these bias forces, requires the differentiation of contact forces from bias forces, a task that is impossible using traditional sensing configurations. 
Emulating the contribution of bias during contact allows the estimation of bias forces and, subsequently, contact forces. By measuring and modeling bias prior to contact, when the only forces on the connector are from bias, a model of the bias source can be made. This model can be used to emulate bias during contact, enabling the differentiation of contact forces and allowing gentle force guided assembly. 
This thesis asserts that identification of and compensation for biasing forces will enable the robotic assembly of complex and fragile connectors that would otherwise be impossible.

/pdf/force-guided-assembly-under-bias-43rjobazkk.pdf

Force guided assembly under bias

Des procedes et des systemes de positionnement et de navigation d'un vehicule autonome (102, 310) permettent au vehicule (102, 310) de se deplacer entre des endroits differents. Une premiere estimation (122) de la position du vehicule (102, 310) est derivee de satellites (132-170, 200-206) d'un systeme mondial de positionnement (100A) et/ou d'un pseudolite (105). Le pseudolite (105) peut etre utilise exclusivement lorsque les satellites (132-170, 200-206) ne sont pas visibles depuis le vehicule (102, 310). Une deuxieme estimation (114) de la position est derivee d'une unite inertielle de reference (904) et/ou d'un compteur kilometrique (902) du vehicule. Les premiere et deuxieme estimations de la position sont combinees et filtrees afin de donner une troisieme estimation (118) de la position. La navigation du vehicule (102, 310) est assuree au moyen des informations (414) sur la position, de donnees (416) de detection et d'evitement d'obstacles et de donnees (908, 910) a bord du vehicule.

Procédé, appareil et système de navigation et de positionnement intégrés pour véhicules

Introduction: On the surface of the moon and Mars there are hundreds of skylights, which are collapsed holes that are believed to lead to underground caves. This research uses Vision, Inertial, and LIDAR sensors to build a high resolution model of a skylight as a landing vehicle flies overhead. We design and fabricate a pit modeling instrument to accomplish this task, implement software, and demonstrate sensing and modeling capability on a suborbital reusable launch vehicle flying over a simulated pit. Future manned colonies on other planets and moons will start in caves for their protection from the harsh space environment, led by the technology developed by this research. While planetary caves have been hypothesized for decades, they have not been plausible exploration candidates due to lack of surface access, difficulty of operation, and obscurity from orbit. Newly discovered planetary pits might be key to accessing subsurface voids, caves, and lava tubes. While the existence of pits is now unambiguous, how to explore them and whether any lead to extended caves is unknown. Pits represent an unparalleled opportunity to access enigmatic subterranean spaces, but their complex geometry makes them impossible to fully observe from orbit. Future robotic exploration missions will target one of these pits[3]. As a spacecraft is about to land, it flies at an altitude under a few hundred meters, traveling at a speed under tens of meters per second. As it flies over a pit during this time, it can collect high quality visual, inertial, and LIDAR data at unprecedented viewing angles from which to model the pit. This same sensor suite is used for precision navigation and terrain hazard avoidance. Reconnaissance from flyover modeling will be used to inform approach and entry paths for rovers[4]. It will be used to characterize geology, select likely locations for inhabitation, and inform follow on exploration. Instrument Design: The pit modeling instrument design accounts for pit geometry, vehicle dynamics during scanning, and required data precision and resolution. The geometry of planetary skylights drives range and field of view of the sensors. Our work specifically studied lunar skylights, which are the best characterized and of high interest. Wagner and Robinson[5] used automated computer vision to detect 200 candidate skylights that are evenly distributed in both location and size. Based on these statistics, the sensor is designed to survey an area that is nominally 50m in radius and 50m in depth. This can model the majority of lunar pits. The dynamical parameters of the landing phase determine sensor range and rate requirements. One example with publicly available data is the NASA ALHAT free flight campaigns on the Morpheus vehicle, which commanded an apogee of 245 meters and a 30-degree glide slope at 12.5 meters per second[1]. This trajectory has been designed to simulate the final landing portion of a robotic lunar mission and can be used as the nominal profile for a skylight scanning mission. From this nominal trajectory, we defined the maximum sensing range to be 260m. This is corresponds to a 45 degree pointing angle above vertical at 180m altitude.

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Flyover Modeling of Planetary Pits

Field robots are mobile, perceptive, forceful robots for duty in unpredictable environments such as unstructured work sites and natural terrain. This paper lays out research issues for this class of robot. Programmed, teleoperated, and cognitive robots are defined and their relevance to field tasks is explored. Several representative application areas for field robots are discussed, including excavation, surface mining, demolition, and planetary exploration. Concluding remarks address future directions for this fledgling discipline.

William Whittaker

Papers

An application of tool insertion using model based vision

Force guided assembly under bias

Procédé, appareil et système de navigation et de positionnement intégrés pour véhicules

Flyover Modeling of Planetary Pits

Robots Beyond the Factory