A model and architecture for situation determination

Automatically determining the situation of an ad-hoc group of people and devices within a smart environment is a significant challenge in pervasive computing systems. Current approaches often rely on an environment expert to correlate the situations that occur with the available sensor data, while other machine learning based approaches require long training periods before the system can be used. Furthermore, situations are commonly recognised at a low-level of granularity, which limits the scope of situation-aware applications. This paper presents a novel approach to situation determination that attempts to overcome these issues by providing a reusable library of general situation specifications that can be easily extended to create new specific situations, and immediately deployed without the need of an environment expert. A proposed architecture of an accompanying situation determination middleware is provided, as well as an analysis of a prototype implementation

Geometric, topological and semantic analysis of multi-building floor plan data

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Architecture, 2006.Includes bibliographical references (leaves 65-67).Generating a comprehensive model of a university campus or other large urban space is a challenging undertaking due to the size, geometric complexity, and levels of rich semantic information contained in inhabited environments. This thesis presents a practical approach to constructing topological models of large environments from labeled floor plan geometry. An exhaustive classification of adjacency types is provided for a university infrastructure including roads, walkways, green-space, and the detailed interior spaces of campus buildings. The system models geospatial features for over 160 buildings within the MIT campus, consisting of more than 800 individual floors, and approximately 36,000 spaces spanning indoor and outdoor terrain. The main motivation is to develop an intuitive, human-centered approach to navigation systems. An application is presented for generating efficient routes between locations on MIT's campus with coverage of both interior and exterior environments. A second application, the MIT WikiMap, aims to generate a more expressive record of the environment by drawing from the knowledge of its inhabitants. The WikiMap provides an interface for collaborative tagging of geographical locations on the MIT campus, designed for interfacing with users to collect semantic data.by Emily J Whiting.S.M

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

A location representation for generating descriptive walking directions

An expressive representation for location is an important component in many applications. However, while many location-aware applications can reason about space at the level of coordinates and containment relationships, they have no way to express the semantics that define how a particular space is used. We present lair, an ontology that addresses this problem by modeling both the geographical relationships between spaces as well as the functional purpose of a given space. We describe how lair was used to create an application that produces walking directions comparable to those given by a person, and a pilot study that evaluated the quality of these directions. We also describe how lair can be used to evaluate other intelligent user interfaces

Remote service provision for connected homes.

This research study proposed to view a remote service delivery system from three distinct perspectives: connected home environments (user perspective), remote service delivery platform (service enabler), and remote service providers (service provider perspective); to establish a holistic view on the requirements of remote service provision to connected home environments. A reference architecture for remote service provision based on the proposed views has been devised, which provides built-in support for an “On-Demand” operating model and facilitate “Freedom of Choice” via different levels of interoperability

A fine-grained geospatial representation and framework for large-scale indoor environments

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 110-112).This thesis describes a system and method for extending the current paradigm of geographic information systems (GIS) to support indoor environments. It introduces features and properties of indoor multi-building environments that do not exist in other geographic environments or are not characterized in existing geospatial models, and proposes a comprehensive representation for describing such spatial environments. Specifically, it presents enhanced notions of spatial containment and graph topology for indoor environments, and extends existing geometric and semantic constructs. Furthermore, it describes a framework to: automatically extract indoor spatial features from a corpus of semi-structured digital floor plans; populate the aforementioned indoor spatial representation with these features; store the spatial data in a descriptive yet extensible data model; and provide mechanisms for dynamically accessing, mutating, augmenting, and distributing the resulting large-scale dataset. Lastly, it showcases an array of applications, and proposes others, which utilize the representation and dataset to provide rich location-based services within indoor environments.by Jonathan Battat.M.Eng

Cognitively-inspired direction giving

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.Includes bibliographical references (p. 133-140).Online mapping services and portable GPS units make it easy to get very detailed driving directions. While these directions are sufficient for an automaton to follow, they do not present a big picture description of the route. As a result, while people can follow these detailed turn-by-turn directions, it can be difficult for them to actually comprehend where they are going. Our goal is to make such directions more comprehensible. Our approach is to apply findings from human spatial cognition, the study of how people conceptualize and organize their knowledge of large-scale space, to create a system that generates written route overviews. Route overviews provide a big picture description of a route, and are intended to supplement the information in turn-by-turn directions. Our route overviews are based on cognitively-inspired design criteria such as: the use of spatial hierarchy, goal-directed descriptions, selective suppression of detail, and the use of the trunk segments and cognitive anchor points along the route. In our experiments, we show that we can make directions more comprehensible independent of the particular places a person knows - by using what we know about how people think about space to structure the way we present spatial information.by Gary Wai Keung Look.Ph.D

Segmentación y posicionamiento 3D de robots móviles en espacios inteligentes mediante redes de cámaras fijas

La presente tesis doctoral surge con el objetivo de realizar contribuciones para la segmentación, identificación y posicionamiento 3D de múltiples robots móviles. Para ello se utiliza un conjunto de cámaras calibradas y sincronizadas entre sí, que se encuentran ubicadas en posiciones fijas del espacio en que se mueven los robots (espacio inteligente). No se contará con ningún conocimiento a priori de la estructura de los robots móviles ni marcas artificiales a bordo de los mismos. Tanto para la segmentación de movimiento como para la estimación de la posición 3D de los robots móviles se propone una solución basada en la minimización de una función objetivo, que incorpora información de todas las cámaras disponibles en el espacio inteligente. Esta función objetivo depende de tres grupos de variables: los contornos que definen la segmentación sobre el plano imagen, los parámetros de movimiento 3D (componentes de la velocidad lineal y angular en el sistema de referencia global) y profundidad de cada punto de la escena al plano imagen. Debido a que la función objetivo depende de tres grupos de variables, para su minimización se emplea un algoritmo greedy, iterativo, entre etapas. En cada una de estas etapas dos de los grupos de variables se suponen conocidos, y se resuelve la ecuación para obtener el restante. De forma previa a la minimización se realiza la inicialización tanto de las curvas que definen los contornos de la segmentación como de la profundidad de cada punto perteneciente a los robots. Además se requiere la estimación del número de robots presentes en la escena. Partiendo de que las cámaras se encuentran en posiciones fijas del espacio inteligente, la inicialización de las curvas se lleva a cabo comparando cada imagen de entrada con un modelo de fondo obtenido previamente. Tanto para el modelado de fondo, como para la comparación de las imágenes de entrada con el mismo se emplea el Análisis de Componentes Principales Generalizado (GPCA). Respecto a la profundidad se emplea Visual Hull 3D (VH3D) para relacionar la información de todas las cámaras disponibles, obteniendo un contorno aproximado de los robots móviles en 3D. Esta reconstrucción de los robots proporciona una buena aproximación de la profundidad inicial de todos los puntos pertenecientes a los robots. Por otro lado, el uso de una versión extendida de la técnica de clasificación k-medias permite obtener una estimación del número de robots presentes en la escena. Tras la segmentación de movimiento y la estimación de la posición 3D de todos los objetos móviles presentes en la escena, se procede a la identificación de los robots móviles. Esta identificación es posible debido a que los robots móviles son agentes controlados por el espacio inteligente, de forma que se cuenta con información acerca de las medidas de los sensores odométricos a bordo de los mismos. Para el seguimiento se propone el uso de un filtro de partículas extendido con proceso de clasificación (XPFCP). La elección de este estimador se debe a que, dado su carácter multimodal, permite el seguimiento de un número variable de elementos (robots móviles) empleando para ello un único estimador, sin necesidad de incrementar el vector de estado. Los resultados obtenidos a la salido del XPFCP son una buena estimación de la posición de los robots móviles en un instante posterior, por lo que esta información se realimenta a la etapa de inicialización de variables, permitiendo reducir el tiempo de procesamiento consumido por la misma. Las diferentes soluciones propuestas a lo largo de la tesis han sido validadas de forma experimental utilizando para ello diferentes secuencias de imágenes (con presencia de diferentes robots, personas, diversos objetos, cambios de iluminación, etc.) adquiridas en el espacio inteligente del Departamento de Electrónica de la Universidad de Alcalá (ISPACE-UAH)