Simultaneous localization and mapping (SLAM) has been extensively researched in past years particularly with regard to range-based or visual-based sensors. Instead of deploying dedicated devices that use visual features, it is more pragmatic to exploit the radio features to achieve this task, due to their ubiquitous nature and the widespread deployment of Wi-Fi wireless network. This paper presents a novel approach for collaborative simultaneous localization and radio fingerprint mapping (C-SLAM-RF) in large unknown indoor environments. The proposed system uses received signal strengths (RSS) from Wi-Fi access points (AP) in the existing infrastructure and pedestrian dead reckoning (PDR) from a smart phone, without a prior knowledge about map or distribution of AP in the environment. We claim a loop closure based on the similarity of the two radio fingerprints. To further improve the performance, we incorporate the turning motion and assign a small uncertainty value to a loop closure if a matched turning is identified. The experiment was done in an area of 130 meters by 70 meters and the results show that our proposed system is capable of estimating the tracks of four users with an accuracy of 0.6 meters with Tango-based PDR and 4.76 meters with a step counter-based PDR.

中文翻译：

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基于Wifi指纹相似度和运动信息的协同SLAM

在过去几年中，同步定位和映射 (SLAM) 得到了广泛的研究，特别是在基于距离或基于视觉的传感器方面。由于其无处不在的性质和 Wi-Fi 无线网络的广泛部署，利用无线电功能来完成这项任务，而不是部署使用视觉特征的专用设备。本文提出了一种在大型未知室内环境中协同同步定位和无线电指纹映射 (C-SLAM-RF) 的新方法。所提议的系统使用来自现有基础设施中的 Wi-Fi 接入点 (AP) 的接收信号强度 (RSS) 和来自智能手机的行人航位推算 (PDR)，而无需事先了解 AP 在环境中的地图或分布。我们根据两个无线电指纹的相似性声明了闭环。为了进一步提高性能，我们结合了转弯运动，并在识别出匹配的转弯时为回环分配一个小的不确定性值。实验是在 130 米 x 70 米的区域内完成的，结果表明我们提出的系统能够估计四个用户的轨迹，基于 Tango 的 PDR 精度为 0.6 米，基于步进计数器的精度为 4.76 米。人民民主共和国。