论文标题
基于WiFi指纹相似性和运动信息的协作大满贯
Collaborative SLAM based on Wifi Fingerprint Similarity and Motion Information
论文作者
论文摘要
在过去的几年中，尤其是在基于范围或基于视觉的传感器方面，对同时的本地化和映射（SLAM）进行了广泛的研究。与其部署使用视觉功能的专用设备，而是由于其无处不在的性质和Wi-Fi Wireless网络的广泛部署而利用无线电功能来实现此任务更为务实。本文介绍了一种在大型未知室内环境中同时定位和无线电指纹映射（C-SLAM-RF）的新方法。拟议的系统使用来自智能手机的现有基础架构和行人死亡计算（PDR）中的Wi-Fi访问点（AP）的接收信号强度（RSS），而没有有关环境中AP的地图或分布的事先了解。我们根据两个无线电指纹的相似性声称循环封闭。为了进一步提高性能，我们将转弯运动结合起来，并为循环闭合分配一个小的不确定性值，如果确定了匹配的转弯。该实验是在130米乘70米的面积上完成的，结果表明，我们提出的系统能够估算四个用户的轨道，其精度为0.6米，基于探戈的PDR和4.76米，并具有基于步骤计数器的PDR。
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.
