Heading Estimation Based on Magnetometer Measurement using LSTM

Autonomous vehicles require the development of sensing technologies and intelligent control of localization capabilities to guide the vehicle in unknown areas. A reliable localization system of accurate positioning and heading information is one of the critical requirements of highly challenging autonomous vehicle technology. This paper proposes an accurate estimation scheme using a Recurrent Neural Network (RNN) architecture for mobile robots in indoor environments via an Inertial Measurement Unit (IMU). The main objective is to assess the potential performance of LSTM or GRU network architecture to obtain estimation values using only low-cost IMU sensor data to create accurate heading angles. The preprocessing stage is carried out to be able to reduce or even eliminate the bad impact of noise generated on each data. The test shows that the model generated from the LSTM network architecture with 32-16 cells of neurons layer can provide heading estimates with MSE value of 0.02 and an accuracy 94.65%.