Context-Aided Sensor Fusion for Enhanced Urban Navigation

Martí, Enrique; Martín, David; García, Juan Francisco Blanco; Escalera, Arturo de la; Molina, José M.; Armingol, José María

doi:10.3390/s121216802

Cited by 41 publications

(34 citation statements)

References 42 publications

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“…This process involves several activities. For instance, finding the available sources of information and their types, gathering the data from these sources, facilitating the fusion (aggregation and derivation) of the different pieces of data [4] [19], building and updating a representation of the environment with this information (what is known as the context) to be used by applications, and triggering actions in actuator devices [8]. This kind of environment is highly dynamic because of the changing behavior of human users, as well as rearrangements in system topology when devices fail, or are added or removed.…”

Section: Introductionmentioning

confidence: 99%

Implementation of context-aware workﬂows with multi-agent systems

et al. 2016

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Cite this article as: Javier Alfonso-Cendón, José M. Fernández-de-Alba, Rubén Fuentes-Fernández, Juan Pavón, Implementation of context-aware workflows with Multi-agent Systems, Neurocomputing, http://dx.doi.org/10.1016/j. neucom.2014.10.098 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Abstract. Systems in Ambient Intelligence (AmI) need to manage workflows that represent users' activities. These workflows can be quite complex, as they may involve multiple participants, both physical and computational, playing different roles. Their execution implies monitoring the development of the activities in the environment, and taking the necessary actions for them and the workflow to reach a certain end. The context-aware approach supports the development of these applications to cope with event processing and regarding information issues. Modeling the actors in these context-aware workflows, where complex decisions and interactions must be considered, can be achieved with multi-agent systems. Agents are autonomous entities with sophisticated and flexible behaviors, which are able to adapt to complex and evolving environments, and to collaborate to reach common goals. This work presents architectural patterns to integrate agents on top of an existing context-aware architecture. This allows an additional abstraction layer on top of context-aware systems, where knowledge management is performed by agents. This approach improves the flexibility of AmI systems and facilitates their design. A case study on guiding users in buildings to their meetings illustrates this approach.

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Section: Introductionmentioning

confidence: 99%

Implementation of context-aware workﬂows with multi-agent systems

et al. 2016

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“…So, the sensor fusion system is based on a loosely coupled architecture which uses GPS position and velocity measurements to aid the INS, typically used in navigation solutions [18]. In this way, the IMU sensors are used extrapolate position, velocity, and attitude at high frequency (50 Hz), while updates from GPS measurements at low frequency (1 Hz) allows refinement of cinematic estimates and inertial sensor biases.…”

Section: Figure 10:attitude Estimation Through Gps Ins and Magnetomementioning

confidence: 99%

A Methodology for Design and Analysis of Sensor Fusion with Real Data in UAV Platforms

García¹,

Molina²,

Trincado³

2018

Preprint

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“…This fused information provides full under standing of the environment. The improvement of positioning in urban environments by means of visual odometry and sensor fusion is our solution for enhanced urban navigation, that is, our aim is to improve the effi ciency of IVVI vehicle positioning in complex urban canyons where GNSS signals are high degraded or even loss for seconds (Martí et al, 2012). We deploy a visual odometry application that uses the movement of the vehicle to provide 2D visual ego motion esti mation.…”

Section: Our Motivations and Its Solutionsmentioning

confidence: 99%

IVVI 2.0: An intelligent vehicle based on computational perception

Martín

García

Musleh

et al. 2014

Expert Systems with Applications

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This is a postprint version of the following published document: Martín, D., et al. (2014) a b s t r a c tThis paper presents the IVVI 2.0 a smart research platform to foster intelligent systems in vehicles. Com putational perception in intelligent transportation systems applications has advantages, such as huge data from vehicle environment, among others, so computer vision systems and laser scanners are the main devices that accomplish this task. Both have been integrated in our intelligent vehicle to develop cutting edge applications to cope with perception difficulties, data processing algorithms, expert knowl edge, and decision making. The long term in vehicle applications, that are presented in this paper, out perform the most significant and fundamental technical limitations, such as, robustness in the face of changing environmental conditions. Our intelligent vehicle operates outdoors with pedestrians and oth ers vehicles, and outperforms illumination variation, i.e.: shadows, low lighting conditions, night vision, among others. So, our applications ensure the suitable robustness and safety in case of a large variety of lighting conditions and complex perception tasks. Some of these complex tasks are overcome by the improvement of other devices, such as, inertial measurement units or differential global positioning sys tems, or perception architectures that accomplish sensor fusion processes in an efficient and safe manner. Both extra devices and architectures enhance the accuracy of computational perception and outreach the properties of each device separately.

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Context-Aided Sensor Fusion for Enhanced Urban Navigation

Cited by 41 publications

References 42 publications

Implementation of context-aware workﬂows with multi-agent systems

Implementation of context-aware workﬂows with multi-agent systems

A Methodology for Design and Analysis of Sensor Fusion with Real Data in UAV Platforms

IVVI 2.0: An intelligent vehicle based on computational perception

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