ComNSense
ComNSense
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DOI：
10.1145/3191733
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发表时间：
2018-03
影响因子：
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通讯作者：
Mohamed Abdelaal;Daniel Reichelt;Frank Dürr;K. Rothermel;L. Runceanu;S. Becker;D. Fritsch
中科院分区：
文献类型：
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作者：
Mohamed Abdelaal;Daniel Reichelt;Frank Dürr;K. Rothermel;L. Runceanu;S. Becker;D. Fritsch
最近，点云已被有效地用于医学成像、城市环境建模和室内建模。在这个领域，谷歌 Tango 和苹果 ARKit 等多个移动平台已经发布，利用 3D 映射、增强现实等技术。在建模应用中，这些现代移动设备为众包点云打开了大门，以分散数据收集的开销。然而，将这些大型点云从资源有限的移动设备上传到后端服务器会消耗过多的能量。因此，这种人群感知系统的参与率可能会受到负面影响。为了应对这一挑战，本文介绍了我们的 ComNSense 方法，该方法可显着降低处理和上传点云的能耗。为此，ComNSense 仅向服务器报告一组提取的几何数据。为了优化几何提取，ComNSense 利用对设计时知识（即结构信息）进行编码的形式语法。为了证明 ComNSense 的有效性，我们进行了多次实验，从两座不同的建筑物收集点云以提取墙壁位置，作为案例研究。我们还评估了 ComNSense 相对于无语法方法的性能。结果表明，在实现相当的检测精度的同时，能耗显着降低。
Recently, point clouds have been efficiently utilized for medical imaging, modeling urban environments, and indoor modeling. In this realm, several mobile platforms, such as Google Tango and Apple ARKit, have been released leveraging 3D mapping, augmented reality, etc. In modeling applications, these modern mobile devices opened the door for crowd-sourcing point clouds to distribute the overhead of data collection. However, uploading these large points clouds from resources-constrained mobile devices to the back-end servers consumes excessive energy. Accordingly, participation rates in such crowd-sensing systems can be negatively influenced. To tackle this challenge, this paper introduces our ComNSense approach that dramatically reduces the energy consumption of processing and uploading point clouds. To this end, ComNSense reports only a set of extracted geometrical data to the servers. To optimize the geometry extraction, ComNSense leverages formal grammars which encode design-time knowledge, i.e. structural information. To demonstrate the effectiveness of ComNSense, we performed several experiments of collecting point clouds from two different buildings to extract the walls location, as a case study. We also assess the performance of ComNSense relative to a grammar-free method. The results showed a significant reduction of the energy consumption while achieving a comparable detection accuracy.