Geo Referenced Dynamic Bayesian Networks for User Positioning on Mobile Systems

Brandherm, Boris; Schwartz, Tim

doi:10.1007/11426646_21

Cited by 24 publications

(14 citation statements)

References 5 publications

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“…Polygons that are defined by vertices from two different levels represent connections such as ramps, stairs, or escalators. Figure 2 (left) shows an example, where the polygon "Corridor.14021" is defined as sequence of vertices with index (1,2,3,4,5,6,7,8). In order to allow for route finding, it is important to know the semantics of connections between polygons.…”

Section: Providing Map Materials For Pedestrian Navigationmentioning

confidence: 99%

“…In order to allow for route finding, it is important to know the semantics of connections between polygons. Thus each edge is attributed by their passability: edges that represent walls or windows are set to be "not passable"; in our example, edge (8, 1) represents a wall and edge (6,7) connects the corridor with the adjacent staircase and is annotated to be "passable for pedestrians." On the right-hand side in Fig.…”

Section: Providing Map Materials For Pedestrian Navigationmentioning

confidence: 99%

“…In [6] and [21] we describe the basics of an egocentric localization system that uses Geo Referenced Dynamic Bayesian Networks (geoDBNs) to determine the position and orientation of a user. Based on this technique we developed a seamless indoor/outdoor positioning system named LORIOT (Location and ORientation in Indoor and Outdoor environmenTs), which uses infrared beacons (IR beacons) and active RFID tags to determine the user's position in indoor environments and a GPS receiver for outdoor localization.…”

Section: Loriotmentioning

confidence: 99%

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Seamless Resource-Adaptive Navigation

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Section: Providing Map Materials For Pedestrian Navigationmentioning

confidence: 99%

Section: Providing Map Materials For Pedestrian Navigationmentioning

confidence: 99%

Section: Loriotmentioning

confidence: 99%

See 1 more Smart Citation

Seamless Resource-Adaptive Navigation

Schwartz

Stahl

Baus

et al. 2010

Resource-Adaptive Cognitive Processes

Self Cite

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“…[2]). The user's location may be determined using IR, RFID, and/or GPS (see [16]). Biosensors (e.g., electrocardiogram (ECG) electromyogram (EMG), electrodermal activity (EDA), and acceleration (ACC) sensors) provide further information about the user's state, which is applied for choosing an appropriate communication channel and for automatically evaluating events (cf.…”

Section: From Sensor Data To Memoriesmentioning

confidence: 99%

SharedLife: Towards Selective Sharing of Augmented Personal Memories

Wahlster

Kröner

Heckmann

2006

Reasoning, Action and Interaction in AI Theories and Systems

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Abstract. The rapid deployment of low-cost ubiquitous sensing devices -including RFID tags and readers, global positioning systems, wireless audio, video, and bio sensors -makes it possible to create instrumented environments and to capture the physical and communicative interaction of an individual with these environments in a digital register. One of the grand challenges of current AI research is to process this multimodal and massive data stream, to recognize, classify, and represent its digital content in a context-sensitive way, and finally to integrate behavior understanding with reasoning and learning about the individual's day by day experiences. This augmented personal memory is always accessible to its owner through an Internet-enabled smartphone using high-speed wireless communication technologies. In this contribution, we discuss how such an augmented personal memory can be built and applied for providing the user with context-related reminders and recommendations in a shopping scenario. With the ultimate goal of supporting communication between individuals and learning from the experiences of others, we apply this novel methods as the basis for a specific way of exploiting memories -the sharing of augmented personal memories in a way that doesn't conflict with privacy constraints.

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“…Bui proposes the Abstract Hidden Markov Memory Model for plan recognition in an intelligent office environment [33]. Geo-referenced DBN are proposed in [34] to fuse sensory data and cope with the problem of inaccurate data.…”

Section: Related Workmentioning

confidence: 99%

Spatially Constrained Grammars for Mobile Intention Recognition

Kiefer

Spatial Cognition VI. Learning, Reasoning, and Talking About Space

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Abstract. Mobile intention recognition is the problem of inferring a mobile agent's intentions from her spatio-temporal behavior. The intentions an agent can have in a specific situation depend on the spatial context, and on the spatially contextualized behavior history. We introduce two spatially constrained grammars that allow for modeling of complex constraints between space and intentions, one based on Context-Free, one based on Tree-Adjoining Grammars. We show which of these formalisms is suited best for frequently occurring intentional patterns. We argue that our grammars are cognitively comprehensible, while at the same time helping to prune the search space for intention recognition.

show abstract

Geo Referenced Dynamic Bayesian Networks for User Positioning on Mobile Systems

Cited by 24 publications

References 5 publications

Seamless Resource-Adaptive Navigation

Seamless Resource-Adaptive Navigation

SharedLife: Towards Selective Sharing of Augmented Personal Memories

Spatially Constrained Grammars for Mobile Intention Recognition

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