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 the Wi-Fi wireless network. This article 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 (APs) in the existing infrastructure and pedestrian dead reckoning (PDR) from a smartphone, 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 m by 70 m and the results show that our proposed system is capable of estimating the tracks of four users with an accuracy of 0.6 m with Tango-based PDR and 4.76 m 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


过去几年，同步定位与地图构建（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。与部署使用视觉功能的专用设备相比，利用无线电功能来完成此任务更为务实，因为它们无处不在，并且 Wi-Fi 无线网络的广泛部署。本文提出了一种在大型未知室内环境中协作同步定位和无线电指纹映射 (C-SLAM-RF) 的新方法。所提出的系统使用现有基础设施中 Wi-Fi 接入点 (AP) 的接收信号强度 (RSS) 和智能手机的行人航位推算 (PDR)，无需事先了解环境中 AP 的地图或分布。我们声称基于两个无线电指纹的相似性的闭环。为了进一步提高性能，我们结合了转动运动，并在识别到匹配的转动时为闭环分配一个小的不确定性值。实验在 130 m x 70 m 的区域中进行，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 m，基于计步器的精度为 4.76 m PDR。