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Leveraging Visual Place Recognition to Improve Indoor Positioning with Limited Availability of WiFi Scans

机译:利用视觉位置识别功能通过有限的WiFi扫描来改善室内定位

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摘要

WiFi-based fingerprinting is promising for practical indoor localization with smartphones because this technique provides absolute estimates of the current position, while the WiFi infrastructure is ubiquitous in the majority of indoor environments. However, the application of WiFi fingerprinting for positioning requires pre-surveyed signal maps and is getting more restricted in the recent generation of smartphones due to changes in security policies. Therefore, we sought new sources of information that can be fused into the existing indoor positioning framework, helping users to pinpoint their position, even with a relatively low-quality, sparse WiFi signal map. In this paper, we demonstrate that such information can be derived from the recognition of camera images. We present a way of transforming qualitative information of image similarity into quantitative constraints that are then fused into the graph-based optimization framework for positioning together with typical pedestrian dead reckoning (PDR) and WiFi fingerprinting constraints. Performance of the improved indoor positioning system is evaluated on different user trajectories logged inside an office building at our University campus. The results demonstrate that introducing additional sensing modality into the positioning system makes it possible to increase accuracy and simultaneously reduce the dependence on the quality of the pre-surveyed WiFi map and the WiFi measurements at run-time.
机译:基于WiFi的指纹识别技术有望在智能手机上进行实际的室内定位,因为该技术可提供当前位置的绝对估计值,而WiFi基础设施在大多数室内环境中无处不在。但是,WiFi指纹识别在定位中的应用需要预先测量的信号图,并且由于安全策略的更改,在最近一代的智能手机中受到越来越多的限制。因此,我们寻求了可以融合到现有室内定位框架中的新信息资源,即使使用质量相对较低的稀疏WiFi信号图,也可以帮助用户查明其位置。在本文中,我们证明了此类信息可以从相机图像的识别中得出。我们提出了一种将图像相似性的定性信息转换为定量约束的方法,然后将其融合到基于图的优化框架中,以与典型的行人航位推算(PDR)和WiFi指纹约束一起定位。改进后的室内定位系统的性能是根据我们大学校园内办公楼内记录的不同用户轨迹进行评估的。结果表明,在定位系统中引入其他传感方式可以提高准确性,同时减少对预先测量的WiFi地图质量和运行时WiFi测量的依赖性。

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