In this paper, we propose rWiFiSLAM, an indoor localisation system based on WiFi ranging measurements. Indoor localisation techniques play an important role in mobile robots when they cannot access good quality GPS signals in indoor environments. Indoor localisation also has many other applications, such as rescue, smart buildings, etc. Inertial Measurement Units (IMU) have been used for Pedestrian Dead Reckoning (PDR) to provide localisation services in the indoor environment as it does not rely on any other signals. Although PDR is a promising technique, it still suffers from unavoidable noise and bias from IMUs in mobile devices. Loop closure is necessary for these scenarios. In this paper, we design an efficient loop closure mechanism based on WiFi ranging measurements along with IMU measurements in a robust pose graph SLAM framework for indoor localisation. One novelty of the proposed method is that we remove the requirement of the full knowledge of the WiFi access point locations, which makes our proposed method feasible for new and/or dynamic environments. We evaluate our designed system in real environments and show the proposed method can achieve sub-meter localisation accuracy and improve the localisation performance by more than 90\% compared with the IMU based PDR.
翻译:在本文中,我们建议使用基于WiFi 测距的室内本地化系统WiFisLASLAM,这是一个基于WiFi测距的室内本地化系统; 室内本地化技术在移动机器人无法获得室内环境中高质量的全球定位系统信号时发挥重要作用; 室内本地化也有许多其他应用,例如救援、智能建筑等; 室内本地化还用于Pedestrian Dead Dead Dead Riskoning(IMU), 用于在室内环境中提供本地化服务, 因为它不依赖任何其他信号; 虽然PDR是一种有希望的技术, 但它仍然受到移动设备中IMU无法避免的噪音和偏差的影响; 这些情景需要 Loop 关闭。 在本文中,我们设计了一个高效的循环封闭机制, 以WiFi测距宽度和IMU测量为基础, 以及一个坚固的SLAM MLAFM框架。 拟议方法的一个新颖之处是, 我们取消了对 WiFi 访问点位置的全部知识的要求, 使我们提出的方法在新的和/或动态环境中可行。 我们评估了我们设计的系统, 显示我们所设计的系统在实际环境中能够比IMU更多实现本地化。