论文标题
深智能手机传感器WIFI融合用于室内定位和跟踪
Deep Smartphone Sensors-WiFi Fusion for Indoor Positioning and Tracking
论文作者
论文摘要
我们解决了室内本地化问题，目的是使用智能手机收集的数据来预测用户的轨迹，该数据使用惯性传感器（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）进行惯性传感器。我们的系统实施了基于深度学习的人死亡估算（Deep PDR）模型，该模型可对用户的相对位置进行高速估计。使用Kalman滤波器，我们使用WiFi纠正PDR的漂移，每次接收WiFi扫描时，都会对用户的绝对位置进行预测。最后，我们使用无MAP投影方法调整Kalman过滤器结果，该方法考虑了环境的物理约束（走廊，门等），并在可能的步行路径上投射预测。我们在IPIN'19室内本地化挑战数据集上测试了我们的管道，并证明它使用挑战评估协议将获胜者的结果提高了20 \％。
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.
