Recently, we experience the increasing prevalence of wearable cameras, some of which feature Wireless Local Area Network (WLAN) connectivity, and the abundance of mobile devices equipped with on-board camera and WLAN modules. Motivated by this fact, this work presents an indoor localization system that leverages both imagery and WLAN data for enabling and supporting a wide variety of envisaged location-aware applications ranging from ambient and assisted living to indoor mobile gaming and retail analytics. The proposed solution integrates two complementary localization approaches, i.e., one based on WLAN and another one based on image location-dependent data, using a fusion engine. Two fusion strategies are developed and investigated to meet different requirements in terms of accuracy, run time, and power consumption. The one is a light-weight threshold-based approach that combines the location outputs of two localization algorithms, namely a WLAN-based algorithm that processes signal strength readings from the surrounding wireless infrastructure using an extended Naive Bayes approach and an image-based algorithm that follows a novel approach based on hierarchical vocabulary tree of SURF (Speeded Up Robust Features) descriptors. The second fusion strategy employs a particle filter algorithm that operates directly on the WLAN and image readings and also includes prior position estimation information in the localization process. Extensive experimental results using real-life data from an indoor office environment indicate that the proposed fusion strategies perform well and are competitive against standalone WLAN and imaged-based algorithms, as well as alternative fusion localization solutions.

中文翻译：

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用于室内用户定位的图像和 WLAN 双峰集成

最近，我们体验到可穿戴相机的日益普及，其中一些具有无线局域网 (WLAN) 连接功能，以及配备机载相机和 WLAN 模块的大量移动设备。受这一事实的启发，这项工作提出了一种室内定位系统，该系统利用图像和 WLAN 数据来启用和支持各种设想的位置感知应用程序，从环境和辅助生活到室内移动游戏和零售分析。所提出的解决方案使用融合引擎集成了两种互补的定位方法，即一种基于WLAN，另一种基于图像位置相关数据。开发和研究了两种融合策略，以满足精度、运行时间和功耗方面的不同要求。一种是基于轻量级阈值的方法，它结合了两种定位算法的位置输出，即一种基于 WLAN 的算法，它使用扩展的朴素贝叶斯方法和基于图像的算法处理来自周围无线基础设施的信号强度读数。遵循一种基于 SURF（加速鲁棒特征）描述符的分层词汇树的新方法。第二种融合策略采用粒子滤波器算法，该算法直接对 WLAN 和图像读数进行操作，并且还包括定位过程中的先验位置估计信息。使用来自室内办公环境的真实数据进行的大量实验结果表明，所提出的融合策略表现良好，并且与独立的 WLAN 和基于图像的算法相比具有竞争力，