Amsters Robin, Demeester Eric, Stevens Nobby, Slaets Peter
Department of Mechanical Engineering, KU Leuven, 3000 Leuven, Belgium.
Department of Electrical Engineering, KU Leuven, 3000 Leuven, Belgium.
Sensors (Basel). 2021 Mar 30;21(7):2394. doi: 10.3390/s21072394.
Most indoor positioning systems require calibration before use. Fingerprinting requires the construction of a signal strength map, while ranging systems need the coordinates of the beacons. Calibration approaches exist for positioning systems that use Wi-Fi, radio frequency identification or ultrawideband. However, few examples are available for the calibration of visible light positioning systems. Most works focused on obtaining the channel model parameters or performed a calibration based on known receiver locations. In this paper, we describe an improved procedure that uses a mobile robot for data collection and is able to obtain a map of the environment with the beacon locations and their identities. Compared to previous work, the error is almost halved. Additionally, this approach does not require prior knowledge of the number of light sources or the receiver location. We demonstrate that the system performs well under a wide range of lighting conditions and investigate the influence of parameters such as the robot trajectory, camera resolution and field of view. Finally, we also close the loop between calibration and positioning and show that our approach has similar or better accuracy than manual calibration.
大多数室内定位系统在使用前都需要进行校准。指纹识别需要构建信号强度图，而测距系统则需要信标的坐标。对于使用Wi-Fi、射频识别或超宽带的定位系统，存在校准方法。然而，可见光定位系统校准的示例却很少。大多数工作集中在获取信道模型参数或基于已知接收器位置进行校准。在本文中，我们描述了一种改进的程序，该程序使用移动机器人进行数据收集，并能够获得带有信标位置及其标识的环境地图。与之前的工作相比，误差几乎减半。此外，这种方法不需要事先知道光源的数量或接收器的位置。我们证明该系统在广泛的光照条件下表现良好，并研究了机器人轨迹、相机分辨率和视野等参数的影响。最后，我们还闭合了校准和定位之间的循环，并表明我们的方法具有与手动校准相似或更好的精度。
