In this work a graph-based, semantic mapping approach for indoor robotics applications is presented, which is extending OpenStreetMap (OSM) with robotic-specific, semantic, topological, and geometrical information. Models for common indoor structures (such as walls, doors, corridors, elevators, etc.) are introduced. The architectural principles support composition with additional domain and application specific knowledge. As an example, a model for an area is introduced and it is explained how this can be used in navigation. A key advantages of the proposed graph-based map representation is that it allows seamless transitions between maps, e.g., indoor and outdoor maps by exploiting the hierarchical structure of the graphs. Finally, the compatibility of the approach with existing, grid-based motion planning algorithms is shown.
Stress is necessary for optimal performance and functioning in daily life. However, when stress exceeds person-specific coping levels, then it begins to negatively impact health and productivity. An automatic stress monitoring system that tracks stress levels based on physical and physiological parameters, can assist the user in maintaining stress within healthy limits. In order to build such a system, we need to develop and test various algorithms on a reference dataset consisting of multimodal stress responses. Such a reference dataset should fulfil requirements derived from results and practices of clinical and empirical research. This paper proposes a set of such requirements to support the establishment of a reference dataset for multimodal human stress detection. The requirements cover person-dependent and technical aspects such as selection of sample population, choice of stress stimuli, inclusion of multiple stress modalities, selection of annotation methods, and selection of data acquisition devices. Existing publicly available stress datasets were evaluated based on criteria derived from the proposed requirements. It was found that none of these datasets completely fulfilled the requirements. Therefore, efforts should be made in the future to establish a reference dataset, satisfying the specified requirements, in order to ensure comparability and reliability of results.
Robots generate large amounts of data which need to be stored in a meaningful way such that they can be used and interpreted later. Such data can be written into log files, but these files lack the querying features and scaling capabilities of modern databases - especially when dealing with multi-robot systems, where the trade-off between availability and consistency has to be resolved. However, there is a plethora of existing databases, each with its own set of features, but none designed with robotic use cases in mind. This work presents three main contributions: (a) structures for benchmarking scenarios with a focus on networked multi-robot architectures, (b) an extensible workbench for benchmarking databases for different scenarios that makes use of Docker containers and (c) a comparison of existing databases given a set of multi-robot use cases to showcase the usage of the framework. The comparison gives indications for choosing an appropriate database.
