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Fast Signals of Opportunity Fingerprint Database Maintenance with Autonomous Unmanned Ground Vehicle for Indoor Positioning

机译：用于室内定位的自动无人地面车辆的机会指纹数据库维护的快速信号

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摘要

Indoor positioning technology based on Received Signal Strength Indicator (RSSI) fingerprints is a potential navigation solution, which has the advantages of simple implementation, low cost and high precision. However, as the radio frequency signals can be easily affected by the environmental change during its transmission, it is quite necessary to build location fingerprint database in advance and update it frequently, thereby guaranteeing the positioning accuracy. At present, the fingerprint database building methods mainly include point collection and line acquisition, both of which are usually labor-intensive and time consuming, especially in a large map area. This paper proposes a fast and efficient location fingerprint database construction and updating method based on a self-developed Unmanned Ground Vehicle (UGV) platform NAVIS, called Automatic Robot Line Collection. A smartphone was installed on NAVIS for collecting indoor Received Signal Strength Indicator (RSSI) fingerprints of Signals of Opportunity (SOP), such as Bluetooth and Wi-Fi. Meanwhile, indoor map was created by 2D LiDAR-based Simultaneous Localization and Mapping (SLAM) technology. The UGV automatically traverse the unknown indoor environment due to a pre-designed full-coverage path planning algorithm. Then, SOP sensors collect location fingerprints and generates grid map during the process of environment-traversing. Finally, location fingerprint database is built or updated by Kriging interpolation. Field tests were carried out to verify the effectiveness and efficiency of our proposed method. The results showed that, compared with the traditional point collection and line collection schemes, the root mean square error of the fingerprinting-based positioning results were reduced by 35.9% and 25.0% in static tests and 30.0% and 21.3% respectively in dynamic tests. Moreover, our UGV can traverse the indoor environment autonomously without human-labor on data acquisition, the efficiency of the automatic robot line collection scheme is 2.65 times and 1.72 times that of the traditional point collection and the traditional line acquisition, respectively.
机译：基于接收信号强度指示器（RSSI）指纹的室内定位技术是一种潜在的导航解决方案，具有实现简单，成本低，精度高的优点。但是，由于射频信号在传输过程中容易受到环境变化的影响，因此有必要事先建立位置指纹数据库并经常进行更新，以保证定位的准确性。目前，指纹数据库的建立方法主要包括点采集和线采集，这两种方法通常都比较费力且费时，特别是在大地图区域。本文提出了一种基于自主研发的无人机地面平台NAVIS的快速高效的位置指纹数据库的构建和更新方法，称为自动机器人线集合。在NAVIS上安装了一个智能手机，用于收集室内机会信号（SOP）的接收信号强度指示器（RSSI）指纹，例如蓝牙和Wi-Fi。同时，通过基于2D LiDAR的同时定位和制图（SLAM）技术创建了室内地图。由于预先设计的全覆盖路径规划算法，UGV自动穿越未知的室内环境。然后，SOP传感器在环境穿越过程中收集位置指纹并生成网格图。最后，通过克里格插值法建立或更新位置指纹数据库。进行了现场测试，以验证我们提出的方法的有效性和效率。结果表明，与传统的点收集和线收集方案相比，基于指纹的定位结果的均方根误差在静态测试中分别降低了35.9％和25.0％，在动态测试中分别降低了30.0％和21.3％。此外，我们的无人飞行器可以在无需人工的情况下自动穿越室内环境，自动机器人线采集方案的效率分别是传统点采集和传统线采集方案的2.65倍和1.72倍。

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