We address the indoor localization problem, where the goal is to predict user's trajectory from the data collected by their smartphone, using inertial sensors such as accelerometer, gyroscope and magnetometer, as well as other environment and network sensors such as barometer and WiFi. Our system implements a deep learning based pedestrian dead reckoning (deep PDR) model that provides a high-rate estimation of the relative position of the user. Using Kalman Filter, we correct the PDR's drift using WiFi that provides a prediction of the user's absolute position each time a WiFi scan is received. Finally, we adjust Kalman Filter results with a map-free projection method that takes into account the physical constraints of the environment (corridors, doors, etc.) and projects the prediction on the possible walkable paths. We test our pipeline on IPIN'19 Indoor Localization challenge dataset and demonstrate that it improves the winner's results by 20\% using the challenge evaluation protocol.

中文翻译：

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深度智能手机传感器-WiFi融合，可用于室内定位和跟踪

我们解决室内定位问题，其目标是使用惯性传感器（例如加速度计，陀螺仪和磁力计）以及其他环境和网络传感器（例如气压计和WiFi），根据其智能手机收集的数据预测用户的轨迹。我们的系统实现了基于深度学习的行人航位推算（deep PDR）模型，该模型可提供用户相对位置的高速率估算。使用卡尔曼滤波器，我们使用WiFi校正PDR的漂移，每次接收到WiFi扫描时，它就可以预测用户的绝对位置。最后，我们使用无地图投影方法调整Kalman滤波结果，该方法考虑了环境（走廊，门等）的物理约束，并将预测投影在可能的步行路径上。