rWiFiSLAM：环境中有效的基于WiFi测距的SLAM系统
rWiFiSLAM: Effective WiFi Ranging based SLAM System in Ambient Environments
摘要：
在本文中，我们提出了rWiFiSLAM，室内定位系统的基础上，WiFi测距测量。当移动机器人在室内环境中无法获得高质量的GPS信号时，室内定位技术在移动机器人中发挥着重要作用。室内定位还有许多其他应用，如救援、智能建筑等。惯性测量单元（IMU）已用于行人航位推算（PDR），以在室内环境中提供定位服务，因为它不依赖于任何其他信号。虽然PDR是一种很有前途的技术，但它仍然受到来自移动设备中的IMU的不可避免的噪声和偏差的影响。对于这些场景，循环闭合是必要的。在本文中，我们设计了一个有效的循环关闭机制的基础上，WiFi测距测量以及IMU测量在一个强大的姿态图SLAM框架的室内定位。所提出的方法的一个新颖之处在于，我们去除了WiFi接入点定位的全部知识的要求，这使得我们提出的方法对于新的和/或动态环境是可行的。我们评估我们设计的系统在真实环境中，并显示所提出的方法可以实现亚米级的定位精度和提高定位性能的90%以上的IMU为基础的PDR相比。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
