RFID-Based Indoor Spatial Query Evaluation with Bayesian Filtering Techniques

People spend a significant amount of time in indoor spaces (e.g., office buildings, subway systems, etc.) in their daily lives. Therefore, it is important to develop efficient indoor spatial query algorithms for supporting various location-based applications. However, indoor spaces differ from outdoor spaces because users have to follow the indoor floor plan for their movements. In addition, positioning in indoor environments is mainly based on sensing devices (e.g., RFID readers) rather than GPS devices. Consequently, we cannot apply existing spatial query evaluation techniques devised for outdoor environments for this new challenge. Because Bayesian filtering techniques can be employed to estimate the state of a system that changes over time using a sequence of noisy measurements made on the system, in this research, we propose the Bayesian filtering-based location inference methods as the basis for evaluating indoor spatial queries with noisy RFID raw data. Furthermore, two novel models, indoor walking graph model and anchor point indexing model, are created for tracking object locations in indoor environments. Based on the inference method and tracking models, we develop innovative indoor range and k nearest neighbor (kNN) query algorithms. We validate our solution through use of both synthetic data and real-world data. Our experimental results show that the proposed algorithms can evaluate indoor spatial queries effectively and efficiently. We open-source the code, data, and floor plan at https://github.com/DataScienceLab18/IndoorToolKit

Hierarchical Graphs as Organisational Principle and Spatial Model Applied to Pedestrian Indoor Navigation

In this thesis, hierarchical graphs are investigated from two different angles – as a general modelling principle for (geo)spatial networks and as a practical means to enhance navigation in buildings. The topics addressed are of interest from a multi-disciplinary point of view, ranging from Computer Science in general over Artificial Intelligence and Computational Geometry in particular to other fields such as Geographic Information Science. Some hierarchical graph models have been previously proposed by the research community, e.g. to cope with the massive size of road networks, or as a conceptual model for human wayfinding. However, there has not yet been a comprehensive, systematic approach for modelling spatial networks with hierarchical graphs. One particular problem is the gap between conceptual models and models which can be readily used in practice. Geospatial data is commonly modelled - if at all - only as a flat graph. Therefore, from a practical point of view, it is important to address the automatic construction of a graph hierarchy based on the predominant data models. The work presented deals with this problem: an automated method for construction is introduced and explained. A particular contribution of my thesis is the proposition to use hierarchical graphs as the basis for an extensible, flexible architecture for modelling various (geo)spatial networks. The proposed approach complements classical graph models very well in the sense that their expressiveness is extended: various graphs originating from different sources can be integrated into a comprehensive, multi-level model. This more sophisticated kind of architecture allows for extending navigation services beyond the borders of one single spatial network to a collection of heterogeneous networks, thus establishing a meta-navigation service. Another point of discussion is the impact of the hierarchy and distribution on graph algorithms. They have to be adapted to properly operate on multi-level hierarchies. By investigating indoor navigation problems in particular, the guiding principles are demonstrated for modelling networks at multiple levels of detail. Complex environments like large public buildings are ideally suited to demonstrate the versatile use of hierarchical graphs and thus to highlight the benefits of the hierarchical approach. Starting from a collection of floor plans, I have developed a systematic method for constructing a multi-level graph hierarchy. The nature of indoor environments, especially their inherent diversity, poses an additional challenge: among others, one must deal with complex, irregular, and/or three-dimensional features. The proposed method is also motivated by practical considerations, such as not only finding shortest/fastest paths across rooms and floors, but also by providing descriptions for these paths which are easily understood by people. Beyond this, two novel aspects of using a hierarchy are discussed: one as an informed heuristic exploiting the specific characteristics of indoor environments in order to enhance classical, general-purpose graph search techniques. At the same time, as a convenient by- product of this method, clusters such as sections and wings can be detected. The other reason is to better deal with irregular, complex-shaped regions in a way that instructions can also be provided for these spaces. Previous approaches have not considered this problem. In summary, the main results of this work are: • hierarchical graphs are introduced as a general spatial data infrastructure. In particular, this architecture allows us to integrate different spatial networks originating from different sources. A small but useful set of operations is proposed for integrating these networks. In order to work in a hierarchical model, classical graph algorithms are generalised. This finding also has implications on the possible integration of separate navigation services and systems; • a novel set of core data structures and algorithms have been devised for modelling indoor environments. They cater to the unique characteristics of these environments and can be specifically used to provide enhanced navigation in buildings. Tested on models of several real buildings from our university, some preliminary but promising results were gained from a prototypical implementation and its application on the models

Umgebungsmodelle und Navigationsdaten für ortsbezogene Dienste in Gebäuden

Ortsbezogene Dienste in Gebäuden (Indoor Location-based Services, I-LBS) sammeln, verarbeiten und stellen Informationen bereit, die in Abhängigkeit der Aufenthaltsorte ihrer Zielobjekte sowie auf der Grundlage eines Umgebungsmodells des jeweiligen Gebäudes berechnet werden. Die Anwendungsbereiche von I-LBS reichen dabei von personenbezogenen Diensten wie z.B. Navigation, ortsbezogenen Benachrichtigungen und Ressourcen-Suche über organisationsbezogene Dienste zur Steuerung und Optimierung von Arbeitsprozessen und Ressourcen bis hin zu Anwendungen, die personen- und organisationsübergreifend Informationen über Zielobjekte und deren jeweilige Umgebungen austauschen. Für die Erzeugung und Nutzung von Umgebungsmodellen ergeben sich mehrere Herausforderungen: Bei der automatischen Erzeugung eines Modells müssen die Eigenschaften eines Gebäudes so erfasst werden, dass dadurch nicht nur Orte gesucht oder kürzeste Wege ermittelt werden können, sondern auch Navigationsdaten für die Berechnung von Navigationsanweisungen zur Verfügung stehen. Dabei stellt sich die Frage, wie ein solches Umgebungsmodell aufgebaut sein muss und wie es sich berechnen lässt. Zur Bereitstellung für verschiedene Dienste wird ein geeignetes Format benötigt, in dem alle relevanten geometrischen, symbolischen und topologischen Informationen über ein Gebäude abgebildet sind. Eine wichtige Rolle spielen dabei auch unterschiedliche Positionierungsverfahren, die miteinander kombiniert werden können und deren heterogene Positionsdaten integriert werden müssen. Für die Realisierung von Indoor-Navigationssystemen besteht sowohl bei stationären als auch bei mobilen Lösungen eine Herausforderung in der Berechnung von dedizierten Navigationsanweisungen. Diese leiten einen Benutzer anhand von textbasierten oder graphischen Hinweisen zu seinem Ziel, dabei können auch Landmarken in die Navigationsanweisungen mit einbezogen werden. Im Rahmen dieser Dissertation werden neue Lösungen für die vorgenannten Probleme aufgezeigt, und die entwickelten Konzepte werden anhand von Simulationen sowie praktischen Umsetzungen diskutiert.Indoor Location-based Services (I-LBS) collect, process and provide information by taking into account the properties of a building and the locations of mobile targets inside. Application scenarios for I-LBS include personal services such as navigation, location-dependent notifications or searching for resources, as well as corporate services for managing and optimizing workflows, and services that combine location data from targets among several organizations. Creating and utilizing a location model involves several challenges: the properties of a building need to be modeled in a way which allows for computations not limited to searching for nearby places or calculating shortest paths, but also include navigation data, which is required for providing navigation instructions. Thus a central question is which information should be included in a location model and how can it be computed. In order to provide location models for different services, an appropriate format is required, which covers the relevant geometric, symbolic and topological information. Also positioning systems play an important part, given that they can be combined with each other and that their heterogeneous position data need to be integrated. Providing intelligible and dedicated navigation instructions poses a challenge for the realization of both stationary and mobile indoor navigation systems. Such instructions guide users to their destination by means of textual or graphical references, which both can be further enriched by incorporating landmarks into the navigation instructions. This dissertation develops new solutions to the above-mentioned problems and the presented concepts are evaluated on the basis of simulations and practical implementations