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2.2 GIS Limitations

Real-time performance and qualitative modeling remain highly challenging, and in situ 3D modeling has become increasingly prominent in current AR research, particularly for mobile scenarios [15]. The main problem of all these applications seems to be the location or geographical information, because a Geographic Information System (GIS) is needed to provide, manage and filter public queries with different levels of accuracy and upgradeable information. In short, we need to link a 3D model to a database that contains all the necessary information associated with it. Furthermore, the introduction of new learning methods using collaborative technologies offers new opportunities to provide educational multimedia content.

While GPS (Global Positioning System) has satisfactory accuracy and performance in open spaces, its quality deteriorates significantly in urban environments. Both the accuracy and the availability of GPS position estimates are reduced by shadowing from buildings and signal reflections. Mobile AR applications for outdoor applications largely rely on the smartphone GPS. Also, GPS provides the user position based on triangulation of signals captured from at least 3 or 4 visible satellites by a GPS receiver. Standard GPS systems have 5m to 30m accuracy due to limitations such as [16]:

Being unavailable (or slow in obtaining position) when satellite signals are absent (such as underground), and when meteorological conditions block transmission, and

Satellites can provide erroneous information about their own position.

Already well known applications are Wikitude, Nokia City Lens, Google Goggles and Metaio Junaio. Todays sensors capabilities in stability and precision have noticeably improved. For example, GPS accuracy is increased with differential GPS or DGPS, which brings the accuracy of readings to within 13 meters of the object, as compared to the 530 meters of normal GPS. DGPS works using a network of stationary GPS receivers [17]. The difference between their predefined position and the position as calculated by the signals from satellites gives the error factor. This error component is then transmitted as an FM signal for the local GPS receivers, enabling them to apply the necessary correction to their readings.

2.3 TICS at University

Recently, experiences of the implementation of TIC in university degrees concluded that digital natives with a periodical activity on networks and chats are better students [18]. The use of VR technologies on practical courses for graduate and undergraduate students aims to develop personal skills [19] introduced in the European Educational Space (EEES), such as a methodical approach to practical engineering problems, teamwork, working in interdisciplinary groups and time management.

In previous publications [20-21] we explained the impact of mobile learning AR technologies introduced in engineering degrees on the academic results of our students, having found that they increased their motivation and satisfaction in classroom.

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