Open AccessDeep heading estimation for pedestrian dead reckoning
Author(s) -
Lin Jia-yi,
Chengming Zou,
Long Lan,
Shanzhi Gu,
Xinshang An
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1656/1/012009
Subject(s) - heading (navigation) , artificial intelligence , computer science , dead reckoning , inertial measurement unit , computer vision , orientation (vector space) , pedestrian , acceleration , step detection , geodesy , engineering , global positioning system , mathematics , geography , telecommunications , physics , geometry , classical mechanics , transport engineering , filter (signal processing)
Currently, the heading estimation could be easily achieved by many built-in direction sensors, such as the smartphone. However, the obtained heading angle is only suitable for situations where an equipped pedestrian’s movement and orientation keep the same, such as normal forward walking and turning. When the pedestrian faces a different direction from his movement, the heading angle remains the pedestrian orientation but not the movement and thus causes a heading estimation error. In this paper, we introduce several related deep learning techniques to explore their respective abilities in heading estimation of the waist-mounted Miniature Inertial Measurement Unit (MIMU). Specifically, this paper adopts two kinds of methods to analyze the data collected from MIMU, include acceleration, angular velocity, or their combination, to predict the heading angle. Firstly, considering the heading estimation is a time series prediction problem actually, this paper introduces the powerful Long Short-Time Memory (LSTM) model. On the other hand, this paper uses the Graph Convolutional Network (GCN) model to consider the relationship between the orientation and the direction of motion at different times. In experiments, we show that the accuracy of the proposed LSTM model in the test set achieved a promising 99.12%. To test our method in the real scenes, this paper designs simulation experiments and mobile terminal tests based on Tensorflow Lite. The experimental results show that the movement heading can be effectively judged based on the waist-mounted sensor data, and has a very significant accuracy.