This thesis discusses deflectometry as a reconstruction method for highly reflecting surfaces. It focuses on deflectometry alone and does not use other reconstruction techniques to supplement with additional data. It explains the measurement process and principle and provides a crash course into an efficient mathematical representation of the principles involved. Using this, it reformulates existing three-dimensional reconstructing methods, expands upon them and develops new ones.
Composite materials, with their higher exposure to dynamic loads, have increasingly been used in aerospace, naval, automotive, sports and other sectors over the last few decades. Dynamic Deformation, Damage and Fracture in Composite Materials and Structures reviews various aspects of dynamic deformation, damage and fracture, mostly in composite laminates and sandwich structures, in a broad range of application fields including aerospace, automotive, defense and sports engineering. As the mechanical behavior and performance of composites varies under different dynamic loading regimes and velocities, the book is divided into sections that examine the different loading regimes and velocities. Part one examine low-velocity loading and part two looks at high-velocity loading. Part three then assesses shock and blast (i.e. contactless) events and the final part focuses on impact (contact) events. As sports applications of composites are linked to a specific subset of dynamic loading regimes, these applications are reviewed in the final part. - Examines dynamic deformation and fracture of composite materials - Covers experimental, analytical and numerical aspects - Addresses important application areas such as aerospace, automotive, wind energy and defence, with a special section on sport applications
Multi-camera systems are being deployed in a variety of vehicles and mobile robots today. To eliminate the need for cost and labor intensive maintenance and calibration, continuous self-calibration is highly desirable. In this book we present such an approach for self-calibration of multi-Camera systems for vehicle surround sensing. In an extensive evaluation we assess our algorithm quantitatively using real-world data.
This book is a comprehensive account of the most recent developments in modern ophthalmic optics. It makes use of the powerful matrix formalism to describe curvature and power, providing a unified view of the optical and geometrical properties of lenses. This unified approach is applicable to the design and properties of not only spectacle lenses, but also contact and intraocular lenses (IOL). The newest developments in lens design, manufacturing and testing are discussed, with an emphasis on the description of free-form technology, which has surpassed traditional manufacturing methods and allows digital lenses to be specifically designed with the unique requirements of the user. Other important topics which are covered include modern lens materials, up-to-date lens measuring techniques, contact and intraocular lenses, progressive power lenses, low vision aids, ocular protection and coatings. Providing a broad overview of recent developments in the field, it is ideal for researchers, manufacturers and practitioners involved in ophthalmic optics.
In this work we present a system to fully automatically create a highly accurate visual feature map from image data acquired from within a moving vehicle. Moreover, a system for high precision self localization is presented. Furthermore, we present a method to automatically learn a visual descriptor. The map relative self localization is centimeter accurate and allows autonomous driving.
This works describes an approach to lane-precise localization on current digital maps. A particle filter fuses data from production vehicle sensors, such as GPS, radar, and camera. Performance evaluations on more than 200 km of data show that the proposed algorithm can reliably determine the current lane. Furthermore, a possible architecture for an intuitive route guidance system based on Augmented Reality is proposed together with a lane-change recommendation for unclear situations.
This work is a contribution to understanding multi-object traffic scenes from video sequences. All data is provided by a camera system which is mounted on top of the autonomous driving platform AnnieWAY. The proposed probabilistic generative model reasons jointly about the 3D scene layout as well as the 3D location and orientation of objects in the scene. In particular, the scene topology, geometry as well as traffic activities are inferred from short video sequences.
This work presents a solution for autonomous vehicles to detect arbitrary moving traffic participants and to precisely determine the motion of the vehicle. The solution is based on three-dimensional images captured with modern range sensors like e.g. high-resolution laser scanners. As result, objects are tracked and a detailed 3D model is built for each object and for the static environment. The performance is demonstrated in challenging urban environments that contain many different objects.
This work presents a behavior planning algorithm for automated driving in urban environments with an uncertain and dynamic nature. The algorithm allows to consider the prediction uncertainty (e.g. different intentions), perception uncertainty (e.g. occlusions) as well as the uncertain interactive behavior of the other agents explicitly. Simulating the most likely future scenarios allows to find an optimal policy online that enables non-conservative planning under uncertainty.
In motion planning for automated vehicles, a thorough uncertainty consideration is crucial to facilitate safe and convenient driving behavior. This work presents three motion planning approaches which are targeted towards the predominant uncertainties in different scenarios, along with an extended safety verification framework. The approaches consider uncertainties from imperfect perception, occlusions and limited sensor range, and also those in the behavior of other traffic participants.
