GraMap: QoS-Aware Indoor Mapping Through Crowd-Sensing Point Clouds with Grammar Support
GraMap: QoS-Aware Indoor Mapping Through Crowd-Sensing Point Clouds with Grammar Support
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DOI：
10.1145/3144457.3144493
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发表时间：
2017-11
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影响因子：
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通讯作者：
Mohamed Abdelaal;Frank Dürr;K. Rothermel;S. Becker;D. Fritsch
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作者：
Mohamed Abdelaal;Frank Dürr;K. Rothermel;S. Becker;D. Fritsch
最近，已经提出了几种方法来自动模拟室内环境。大多数这样的努力主要依靠人群来感知数据，如运动轨迹、图像和WiFi足迹。然而，通常需要大的数据集来导出精确的室内模型，这可能负面地影响参与人群感测系统的移动的设备的能量效率。此外，上述数据类型几乎不适合于导出3D室内模型。为了克服这些挑战，我们提出了GraMap，一种通过人群感知3D点云的QoS感知自动室内建模方法。GraMap利用最近开发的传感器融合机制，即Tango技术，从人群中协作收集点云。之后，一组后端服务器提取所需的几何信息以导出室内模型。为了提高移动的设备的能源效率，GraMap沿着3D数据压缩来执行数据质量保证。具体来说，我们提出了一个概率质量模型-实现的移动的设备-以确保高质量的捕获点云。通过这种方式，我们通过避免由于上传低质量点云而导致的重复感测查询来节省能量。然而，由此产生的室内模型可能仍然遭受不完整性和不准确性。因此，GraMap利用对设计时知识（即有关建筑物的结构信息）进行编码的形式语法来提高派生模型的质量。为了证明GraMap的有效性，我们实现了一个人群感知Android应用程序来收集志愿者的点云。我们表明，GraMap导出高精度模型，同时降低了预处理和报告点云的能源成本。
Recently, several approaches have been proposed to automatically model indoor environments. Most of such efforts principally rely on the crowd to sense data such as motion traces, images, and WiFi footprints. However, large datasets are usually required to derive precise indoor models which can negatively affect the energy efficiency of the mobile devices participating in the crowd-sensing system. Furthermore, the aforementioned data types are hardly suitable for deriving 3D indoor models. To overcome these challenges, we propose GraMap, a QoS-aware automatic indoor modeling approach through crowd-sensing 3D point clouds. GraMap exploits a recently-developed sensors fusion mechanism, namely Tango technology, to cooperatively collect point clouds from the crowd. Afterward, a set of backend servers extracts the required geometrical information to derive indoor models. For the sake of improving the energy efficiency of the mobile devices, GraMap performs data quality assurance along with 3D data compression. Specifically, we propose a probabilistic quality model---implemented on the mobile devices---to ensure high-quality of the captured point clouds. In this manner, we conserve energy via sidestepping the repetition of sensing queries due to uploading low-quality point clouds. Nevertheless, the resultant indoor models may still suffer from incompleteness and inaccuracies. Therefore, GraMap leverages formal grammars which encode design-time knowledge, i.e. structural information about the building, to enhance the quality of the derived models. To demonstrate the effectiveness of GraMap, we implemented a crowd-sensing Android App to collect point clouds from volunteers. We show that GraMap derives highly-accurate models while reducing the energy costs of pre-processing and reporting the point clouds.