Site-specific radio frequency (RF) propagation prediction increasingly relies on models built from visual data such as cameras and LIDAR sensors. When operating in dynamic settings, the environment may only be partially observed. This paper introduces a method to extract statistical channel models, given partial observations of the surrounding environment. We propose a simple heuristic algorithm that performs ray tracing on the partial environment and then uses machine-learning trained predictors to estimate the channel and its uncertainty from features extracted from the partial ray tracing results. It is shown that the proposed method can interpolate between fully statistical models when no partial information is available and fully deterministic models when the environment is completely observed. The method can also capture the degree of uncertainty of the propagation predictions depending on the amount of region that has been explored. The methodology is demonstrated in a robotic navigation application simulated on a set of indoor maps with detailed models constructed using state-of-the-art navigation, simultaneous localization and mapping (SLAM), and computer vision methods.
翻译:特定地点的无线电频率(RF)传播预测越来越依赖从照相机和LIDAR传感器等视觉数据中建立的模式。当在动态环境中运行时,只能部分观测环境。本文件介绍了一种根据对周围环境的局部观测来提取统计频道模型的方法。我们建议了一种简单的超光速算法,对部分环境进行射线跟踪,然后使用经过机器学习的训练有素的预测器来估计频道及其从部分射线跟踪结果中提取的特征所产生的不确定性。显示,在没有局部信息的情况下,拟议方法可以在完全统计模型之间相互交叉,而在完全观测环境时,完全确定模型之间相互交叉。该方法还可以根据所探索的区域数量来捕捉传播预测的不确定性程度。该方法在一套室内地图上模拟的机器人导航应用中演示了使用最新导航、同步本地化和绘图(SLAM)和计算机视觉方法构建的详细模型。