Navigation based on symbolic space models

Existing navigation systems are very appropriate for car navigation, but lack support for convenient pedestrian navigation and cannot be used indoors due to GPS limitations. In addition, the creation and the maintenance of the required models are costly and time consuming, and are usually based on proprietary data structures. In this paper we describe a navigation system based on a human inspired symbolic space model. We argue that symbolic space models are much easier to create and to maintain, and that they can support routing applications based on self-locating through the recognition of nearby features. Our symbolic space model is supported by a federation of servers where the spatial descriptions are stored, and which provide interfaces for feeding and querying the model. Local models residing in different servers may be connected between them, thus contributing to the system scalability.Fundação para a Ciência e a Tecnologia (FCT

Automatic classification of location contexts with decision trees

Location contexts are geographic regions, with well defined boundaries, that can be used to characterize the context of the persons lying inside them. In this paper we describe a process that exploits the increasing availability of geographic data to automatically create and classify location contexts. The pro-posed process generates new geographic regions from a database of Points Of Interest through the use of spatial clustering techniques, and classifies them automatically using a decision tree based method. Some preliminary results demonstrate the validity of this approach, while suggesting that a richer geographic database could produce location contexts of higher quality.Fundação para a Ciência e a Tecnologia (FCT)

Decentralized Probabilistic World Modeling with Cooperative Sensing

Drawing on the projected increase in computing power, solid-state storage and network communication capacity to be available on personal mobile devices, we propose to build and maintain without prior knowledge a fully distributed decentralized large-scale model of the physical world around us using probabilistic methods. We envisage that, by using the multimodal sensing capabilities of modern personal devices, such a probabilistic world model can be constructed as a collaborative effort of a community of participants, where the model data is redundantly stored on individual devices and updated and refined through short-range wireless peer-to-peer communication. Every device holds model data describing its current surroundings, and obtains model data from others when moving into unknown territory. The model represents common spatio-temporal patterns as observed by multiple participants, so that rogue participants can not easily insert false data and only patterns of general applicability dominate. This paper aims to describe – at a conceptual level – an approach for building such a distributed world model. As one possible world modeling approach, it discusses compositional hierarchies, to fuse the data from multiple sensors available on mobile devices in a bottom-up way. Furthermore, it focuses on the intertwining between building a decentralized cooperative world model and the opportunistic communication between participants

World Modeling for Intelligent Autonomous Systems

The functioning of intelligent autonomous systems requires constant situation awareness and cognition analysis. Thus, it needs a memory structure that contains a description of the surrounding environment (world model) and serves as a central information hub. This book presents a row of theoretical and experimental results in the field of world modeling. This includes areas of dynamic and prior knowledge modeling, information fusion, management and qualitative/quantitative information analysis

World Modeling for Intelligent Autonomous Systems

The functioning of intelligent autonomous systems requires constant situation awareness and cognition analysis. Thus, it needs a memory structure that contains a description of the surrounding environment (world model) and serves as a central information hub. This book presents a row of theoretical and experimental results in the field of world modeling. This includes areas of dynamic and prior knowledge modeling, information fusion, management and qualitative/quantitative information analysis

World Modeling for Intelligent Autonomous Systems

Within the scope of this work, we have attained a row of theoretical and experimental results in the field of world modeling as well as gathered significant experience and expertise. The covered topics include concepts and approaches for dynamic and prior knowledge modeling, information association, fusion and management as well as their practical realization and experimental evaluation

Dynamic World Models from Ray-tracing

Context-aware computing systems demand an accurate and up-to-date world model which computationally represents the environment they oversee. Systems to date tend to have small-scale implementations with hand-programmed world models