Advanced augmented reality (AR) games such as Pokemon Go attract millions and millions of players because they integrate various sensors to impress excellent user experiences on mobile devices. In this article, for energy saving on the mobile devices, we can use an accelerometer and a magnetometer to estimate the current user's location approaching interest of points on a game map without active GPS sensors and wireless network. In addition, we create an effective data structure with R-tree and adopt deep learning to efficiently answer the location query of Pokemons on the game location database. Our algorithm outperforms previous approaches in not only smaller running time but also in the better power consumption for the AR games on mobile devices.

中文翻译：

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移动设备上 Pokemon GO 的深度游戏位置获取

先进的增强现实 (AR) 游戏，例如Pokemon Go 吸引了数以百万计的玩家，因为它们集成了各种传感器，可以在移动设备上提供出色的用户体验。在本文中，为了在移动设备上节省能源，我们可以使用加速度计和磁力计来估计当前用户的位置接近游戏地图上感兴趣的点，而无需主动 GPS 传感器和无线网络。此外，我们利用R树创建了有效的数据结构，并采用深度学习来高效地回答神奇宝贝在游戏位置数据库上的位置查询。我们的算法优于以前的方法，不仅运行时间更短，而且移动设备上的 AR 游戏的功耗也更低。