The application of autonomous vehicles becomes more and more essential. LiDAR sensors play a significant role in environmental perception. However, rain-, fog-, snow- or dust particles disturb the point clouds and affect LiDAR-based map reconstruction and object detection algorithms. Widely scattered particles can be filtered using statistics. In contrast, dense fog or dust clouds are more challenging to deal with as it is difficult to distinguish them based on their statistics from parts of the environment. Literature provides numerous examples of particle filter applications. Still, the applicability in challenging environments as the off-road domain remains unclear. Therefore, this paper reviews and tests state-of-the-art particle filters on-and off-road scenes.

Robust off-road perception for autonomous navigation is hard to achieve. Versatile environments, different hardware, and numerous disturbances limit the perceptional portability in changing applications and cross-platform. This contribution proposes sensor-fusion considering the data quality of uncertain sensors to increase the classification and mapping components’ perceptual robustness. The resulting benefits on perception are demonstrated using the autonomous off-road robot U5023.

This paper describes a novel behavior-based local navigation approach for rough off-road scenarios. Trajectory candidates are generated based on vehicle kinematics and dynamics as well as the desired global trajectory. In contrast to on-road local navigation approaches, the work at hand proposes the use of a shiftable elevation grid map instead of occupancy maps since traversability in rough terrains does not only depend on location, but also on the robot's orientation. The traversability is evaluated by determining tire contact points with the terrain to take various different safety and efficiency aspects like underbody collisions and rollover risk into account. By exploiting the behavior-based control paradigm, the navigation approach can be easily extended and its robustness is shown in experimental evaluations using an Unimog U5023.

In this paper we present four informed natural language watermark embedding methods, which operate on the lexical and syntactic layer of German texts. Our scheme provides several benefits in comparison to state-of-the-art approaches, as for instance that it is not relying on complex NLP operations like full sentence parsing, word sense disambiguation, named entity recognition or semantic role parsing. Even rich lexical resources (e.g. WordNet or the Collins thesaurus), which play an essential role in many previous approches, are unnecessary for our system. Instead, our methods require only a Part-Of-Speech Tagger, simple wordlists that act as black- and whitelists and a trained classifier, which automatically predicts the ability of potential lexical or syntactic patterns to carry portions of the watermark message. Besides this, a part of the proposed methods can be easily adapted into other Indo-European languages, since the grammar rules the methods rely on are not restricted only to the German language. Because the methods perform only lexical and minor syntactic transformations, the watermarked text is not affected by grammatical distortion and simultaneously the meaning of the text is preserved in 82.14% of the cases.

In this paper, we introduce a novel metadata model for describing scalable and interactive TV services that can be enriched with supplemental multimedia information. The model allows users to access such TV services not only via their traditional TV sets, but also via additional mobile devices like TabletPCs or PDAs. To achieve this, we segment the traditional linear program into sub-components, while separating device-independent and device-specific metadata. A realization of this model builds on the existing standards of TV-Anytime, MPEG-7 and MPEG-21. The model achieves a step towards the "Connected world" vision of the second specification phase of the TV-Anytime forum.

One of the main challenges facing Web-based multimedia content creators today is the development of cost-effective digital media content for courseware that is reusable and interoperable. Given this, we propose the design and implementation of a model to manage the multimedia and metadata within a system of related topics in a course that is delivered via electronic lectures. This model not only supports the re-purposing and interchange of a course's digital media content, but also preserves the semantic dependencies and associated media-mapping, of pre-requisite and co-requisite knowledge within a course. Coarse grain or topic-based segmentations of an electronic lecture are used as building blocks to automatically create new, media-rich, electronic lecture experiences of interest to a user and subject to the constraints imposed by the model.

LectureLounge is a research platform and a system to automatically and non-invasively capture, analyze, annotate, index, archive and publish live-presentations.On one hand LectureLounge covers the complete value chain from capturing to publication und the focus of automation and so is more than a media-on-demand system in operation. On the other hand LectureLounge is also a research platform designed and built to integrate modules of all kinds, for example, fully-automated capture systems, video/image feature extraction and analysis applications or MPEG-4/7 streaming modules.