HINNet plus HeadSLAM: Robust Inertial Navigation With Machine Learning for Long-Term Stable Tracking

被引：3

作者：

Hou, Xinyu ^{[1
]}

Bergmann, Jeroen ^{[1
]}

机构：

[1] Univ Oxford, Dept Engn Sci, Oxford OX1 3PJ, England

来源：

IEEE SENSORS LETTERS | 2023年 / 7卷 / 08期

基金：

英国工程与自然科学研究理事会;

关键词：

Trajectory; Legged locomotion; Inertial navigation; Pedestrians; Odometry; Calibration; Tracking; Sensor applications; deep neural network (DNN); inertial measurement unit (IMU); inertial navigation; machine learning; pedestrian dead reckoning (PDR); SLAM; wearable sensors;

D O I：

10.1109/LSENS.2023.3294553

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In recent years, human position tracking with wearable sensors has been rapidly developed and shown great potential for applications within healthcare, smart homes, sports, and emergency services. Unlike tracking researches with sensors on the foot, human positioning studies with head-mounted sensors are fewer and still remain problems that have not been solved. We have proposed two studies solve part of the problems separately: HINNet is able to track people with free head rotations; HeadSLAM allows long-term tracking with stable errors. In this letter, to allow free head rotations meanwhile support long-term tracking, HINNet is combined with HeadSLAM and tested. The result shows that the combination could effectively distinguish head rotations and keep a low and stable position error in long-term tracking, with an absolute trajectory error (ATE) of 2.69 m and relative trajectory error (RTE) of 3.52 m.

引用

页数：4

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