Free-walking:Pedestrian inertial navigation based on dual foot-mounted IMU  

作　　者：Qu Wang Meixia Fu Jianquan Wang Lei Sun Rong Huang Xianda Li Zhuqing Jiang Yan Huang Changhui Jiang 

机构地区：[1]School of Automation Science and Electrical Engineering,University of Science and Technology Beijing,Beijing,100083,China [2]Shunde Innovation School,University of Science and Technology Beijing,Fo Shan,528399,China [3]Research Institute of China Unicom,Beijing,100190,China [4]School of Artificial Intelligence,Beijing University of Posts and Telecommunications,Beijing,100876,China [5]Department of Automation Tsinghua University,Beijing,100084,China [6]GEOLOC Laboratory,Universite Gustave Eiffel,Paris,77454,France

出　　处：《Defence Technology（防务技术）》2024年第3期573-587,共15页Defence Technology

基　　金：supported in part by National Key Research and Development Program under Grant No.2020YFB1708800;China Postdoctoral Science Foundation under Grant No.2021M700385;Guang Dong Basic and Applied Basic Research Foundation under Grant No.2021A1515110577;Guangdong Key Research and Development Program under Grant No.2020B0101130007;Central Guidance on Local Science and Technology Development Fund of Shanxi Province under Grant No.YDZJSX2022B019;Fundamental Research Funds for Central Universities under Grant No.FRF-MP-20-37;Interdisciplinary Research Project for Young Teachers of USTB(Fundamental Research Funds for the Central Universities)under Grant No.FRF-IDRY-21-005;National Natural Science Foundation of China under Grant No.62002026。

摘　　要：The inertial navigation system(INS),which is frequently used in emergency rescue operations and other situations,has the benefits of not relying on infrastructure,high positioning frequency,and strong real-time performance.However,the intricate and unpredictable pedestrian motion patterns lead the INS localization error to significantly diverge with time.This paper aims to enhance the accuracy of zero-velocity interval(ZVI)detection and reduce the heading and altitude drift of foot-mounted INS via deep learning and equation constraint of dual feet.Aiming at the observational noise problem of low-cost inertial sensors,we utilize a denoising autoencoder to automatically eliminate the inherent noise.Aiming at the problem that inaccurate detection of the ZVI detection results in obvious displacement error,we propose a sample-level ZVI detection algorithm based on the U-Net neural network,which effectively solves the problem of mislabeling caused by sliding windows.Aiming at the problem that Zero-Velocity Update(ZUPT)cannot suppress heading and altitude error,we propose a bipedal INS method based on the equation constraint and ellipsoid constraint,which uses foot-to-foot distance as a new observation to correct heading and altitude error.We conduct extensive and well-designed experiments to evaluate the performance of the proposed method.The experimental results indicate that the position error of our proposed method did not exceed 0.83% of the total traveled distance.

关 键 词：Indoor positioning Inertial navigation system(INS) Zero-velocity update(ZUPT) Internet of things(IoTs) Location-based service(LBS) 

分 类 号：TN96[电子电信—信号与信息处理]