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The main focus of this work is the development of new methods for the self-calibration of a rigid stereo camera system. However, many of the algorithms introduced here have a wider impact, particularly in robot hand-eye calibration with all its different areas of application. Stereo self-calibration refers to the computation of the intrinsic and extrinsic parameters of a stereo rig using neither a priori knowledge on the movement of the rig nor on the geometry of the observed scene.
The stereo parameters obtained by self-calibration, namely rotation and translation from left to right camera, are used for computing depth maps for both images, which are applied for rendering correctly occluded virtual objects into a real scene (Augmented Reality).
The proposed methods were evaluated on real and synthetic data and compared to algorithms from the literature. In addition to a stereo rig, an optical tracking system with a camera mounted on an endoscope was calibrated without a calibration pattern using the proposed extended hand-eye calibration algorithm.
The self-calibration methods developed in this work have a number of features, which make them easily applicable in practice: They rely on temporal feature tracking only, as this monocular tracking in a continuous image sequence is much easier than left-to-right tracking when the camera parameters are still unknown.
Intrinsic and extrinsic camera parameters are computed during the self-calibration process, i.e., no calibration pattern is required. The proposed stereo self-calibration approach can also be used for extended hand-eye calibration, where the eye poses are obtained by structure-from-motion rather than from a calibration pattern.
An inherent problem to hand-eye calibration is that it requires at least two general movements of the cameras in order to compute the rigid transformation.
If the motion is not general enough, only a part of the parameters can be obtained, which would not be sufficient for computing depth maps. Therefore, a main part of this work discusses methods for data selection that increase the robustness of hand-eye calibration. Different new approaches are shown, the most successful ones being based on vector quantization.
The data selection algorithms developed in this work can not only be used for stereo self-calibration, but also for classic robot hand-eye calibration, and they are independent of the actually used hand-eye calibration algorithm.
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The number of embedded systems in our daily lives that are distributed, hidden, and ubiquitous continues to increase. Many of them are safety-critical. To provide additional or better functionalities, they are becoming more and more complex, which makes it difficult to guarantee safety. It is undisputed that safety must be considered before the start of development, continue until decommissioning, and is particularly important during the design of the system and software architecture. An architecture must be able to avoid, detect, or mitigate all dangerous failures to a sufficient degree. For this purpose, the architectural design must be guided and verified by safety analyses. However, state-of-the-art component-oriented or model-based architectural design approaches use different levels of abstraction to handle complexity. So, safety analyses must also be applied on different levels of abstraction, and it must be checked and guaranteed that they are consistent with each other, which is not supported by standard safety analyses. In this paper, we present a consistency check for CFTs that automatically detects commonalities and inconsistencies between fault trees of different levels of abstraction. This facilitates the application of safety analyses in top-down architectural designs and reduces effort.
In safety analysis for safety-critical embedded systems, methods such as FMEA and fault trees (FT) are strongly established in practice. However, the current shift towards model-based development has resulted in various new safety analysis methods, such as Component Integrated Fault Trees (CFT). Industry demands to know the benefits of these new methods. To compare CFT to FT, we conducted a controlled experiment in which 18 participants from industry and academia had to apply each method to safety modeling tasks from the avionics domain.
Although the analysis of the solutions showed that the use of CFT did not yield a significantly different number of correct or incorrect solutions, the participants subjectively rated the modeling capacities of CFT significantly higher in terms of model consistency, clarity, and maintainability. The results are promising for the potential of CFT as a model-based approach.
Safety assurance is a major challenge in the design of modern embedded systems that has become increasingly difficult in recent years. Growing system sizes and the rise of Cyber-Physical systems confront safety engineers with large sets of configurations to be analyzed. Current approaches are usually carried out at design time and do not address the need for automated assessments in the field. With Component Fault Trees (CFTs) there exists a component-based methodology that enables an efficient modular composition of safety artifacts. The combined model is a system-level CFT that can be analyzed by means of popular Fault Tree Analysis techniques that are widely accepted in the industry. However, when composing models, their interfacing elements must be connected manually which impedes the automation of the procedure. In this work, we introduce the notion of flow types that represent a particular kind of component interaction and define a taxonomy of related failure behavior. By annotating CFTs with types, a machine-readable vocabulary is provided that allows for an automated interconnection of their interfaces. This way, the automatic composition of models according to system architecture is enabled, allowing for automated safety assessments on system-level. We demonstrate the feasibility of our approach using an example ethylene vaporization unit.
We introduce a model for extractive meeting summarization based on the hypothesis that utterances convey bits of information, or concepts. Using keyphrases as concepts weighted by frequency, and an integer linear program to determine the best set of utterances, that is, covering as many concepts as possible while satisfying a length constraint, we achieve ROUGE scores at least as good as a ROUGE-based oracle derived from human summaries. This brings us to a critical discussion of ROUGE and the future of extractive meeting summarization.
This paper presents an unsupervised, graph based approach for extractive summarization of meetings. Graph based methods such as TextRank have been used for sentence extraction from news articles. These methods model text as a graph with sentences as nodes and edges based on word overlap. A sentence node is then ranked according to its similarity with other nodes. The spontaneous speech in meetings leads to incomplete, informed sentences with high redundancy and calls for additional measures to extract relevant sentences. We propose an extension of the TextRank algorithm that clusters the meeting utterances and uses these clusters to construct the graph. We evaluate this method on the AM I meeting corpus and show a significant improvement over TextRank and other baseline methods.