MapSense

IF 3.5 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Mohamed Abdelaal, S. Sekar, Frank Dürr, K. Rothermel, S. Becker, D. Fritsch
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引用次数: 1

Abstract

Recently, indoor modeling has gained increased attention, thanks to the immense need for realizing efficient indoor location-based services. Indoor environments differ from outdoor spaces in two aspects: spaces are smaller and there are many structural objects such as walls, doors, and furniture. To model the indoor environments in a proper manner, novel data acquisition concepts and data modeling algorithms have been devised to meet the requirements of indoor spatial applications. In this realm, several research efforts have been exerted. Nevertheless, these efforts mostly suffer either from adopting impractical data acquisition methods or from being limited to 2D modeling. To overcome these limitations, we introduce the MapSense approach, which automatically derives indoor models from 3D point clouds collected by individuals using mobile devices, such as Google Tango, Apple ARKit, and Microsoft HoloLens. To this end, MapSense leverages several computer vision and machine learning algorithms for precisely inferring the structural objects. In MapSense, we mainly focus on improving the modeling accuracy through adopting formal grammars that encode design-time knowledge, i.e., structural information about the building. In addition to modeling accuracy, MapSense considers the energy overhead on the mobile devices via developing a probabilistic quality model through which the mobile devices solely upload high-quality point clouds to the crowd-sensing servers. To demonstrate the performance of MapSense, we implemented a crowd-sensing Android App to collect 3D point clouds from two different buildings by six volunteers. The results showed that MapSense can accurately infer the various structural objects while drastically reducing the energy overhead on the mobile devices.
最近,由于实现高效的室内定位服务的巨大需求,室内建模得到了越来越多的关注。室内环境与室外空间的区别在于两个方面:空间更小,有许多结构物体,如墙壁,门和家具。为了对室内环境进行适当的建模,设计了新的数据采集概念和数据建模算法,以满足室内空间应用的要求。在这一领域,已经进行了一些研究工作。然而,这些努力大多受到采用不切实际的数据采集方法或限于二维建模的影响。为了克服这些限制,我们引入了MapSense方法,该方法自动从使用移动设备(如Google Tango、Apple ARKit和Microsoft HoloLens)的个人收集的3D点云中提取室内模型。为此,MapSense利用几种计算机视觉和机器学习算法来精确推断结构对象。在MapSense中,我们主要通过采用编码设计时知识(即建筑物的结构信息)的形式语法来提高建模精度。除了建模精度之外,MapSense还通过开发一个概率质量模型来考虑移动设备的能量开销,通过该模型,移动设备可以单独将高质量的点云上传到人群感知服务器。为了演示MapSense的性能,我们实现了一个人群传感Android应用程序,由六名志愿者从两座不同的建筑物中收集3D点云。结果表明,MapSense可以准确地推断出各种结构对象,同时大大减少了移动设备上的能量开销。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
5.20
自引率
3.70%
发文量
0
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