The paper proposes a multi-modal sensor fusion algorithm that fuses WiFi, IMU, and floorplan information to infer an accurate and dense location history in indoor environments. The algorithm uses 1) an inertial navigation algorithm to estimate a relative motion trajectory from IMU sensor data; 2) a WiFi-based localization API in industry to obtain positional constraints and geo-localize the trajectory; and 3) a convolutional neural network to refine the location history to be consistent with the floorplan. We have developed a data acquisition app to build a new dataset with WiFi, IMU, and floorplan data with ground-truth positions at 4 university buildings and 3 shopping malls. Our qualitative and quantitative evaluations demonstrate that the proposed system is able to produce twice as accurate and a few orders of magnitude denser location history than the current standard, while requiring minimal additional energy consumption. We will publicly share our code, data and models.
翻译:本文建议采用多式传感器聚合算法,将WiFi、IMU和地平图信息结合起来,以推断室内环境的准确和密集位置历史。算法使用:(1)惯性导航算法,根据IMU传感器数据估计相对运动轨迹;(2)基于WiFi的工业本地化API,以获得位置限制和轨道地理定位;(3)进化神经网络,以根据地平图改进位置历史。我们开发了一个数据获取应用程序,以在4所大学建筑和3个购物中心用地平线定位建立一个新的数据集。我们的定性和定量评估表明,拟议的系统能够产生比目前标准更准确和几级密度的密度位置历史,同时需要最低限度的额外能源消耗。我们将公开分享我们的代码、数据和模型。