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Abstract
3D map for mobile devices provide more realistic view of an environment and serves as better navigation aid. Previous research studies shows differences in 3D maps effect on acquiring of spatial knowledge. This is attributed to the differences in mobile device computational capabilities. Crucial to this, is the time it takes for 3D map dataset to be rendered for a required complete navigation task. Different findings suggest different approach on solving the problem of time require for both in-core (inside mobile) and out-core (remote) rendering of 3D dataset. Unfortunately, studies on analytical techniques required to shows the impact of computational resources required for the use of 3D map on mobile device were neglected by the research communities. This paper uses Support Vector Machine (SVM) to analytically classify mobile device computational capabilities required for 3D map that will be suitable for use as navigation aid. Fifty different Smart phones were categorized on the bases of their Graphical Processing Unit (GPU), display resolution, memory and size. The result of the proposed classification shows high accuracy
Highlights
Mobile devices, especially smart phones are able to render 3D maps which provide a more realistic view of the environment and serve as an improved navigation aid [1]
The Graphical Processing Unit (GPU) class is determined by three attributes to produce the predicted results at different levels of the experiment
This research proposes the classification of computational resources necessary for 3D maps that can be used in mobile devices aiding navigation
Summary
Especially smart phones are able to render 3D maps which provide a more realistic view of the environment and serve as an improved navigation aid [1]. The main advantage of 3D maps is that they enhance visual quality and, when used as a navigation aid, improve navigation practices [2]. The majority of studies add that visualization is a major control variable needed to solve navigation tasks with the aid of a 3D map view [1,2,3,4,5,6,7,8,9]. The reason for using this algorithm is because it imitates the real-life process of demarcating two or more elements for optimization to obtain the best solution for understanding the required computational resources for a 3D map that is suitable for aiding navigation using mobile devices
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