Model-Based Deep Learning for Low-Cost IMU Dead Reckoning of Wheeled Mobile Robot

Abstract

Low-cost inertial measurement units (IMUs) suffer from low sensitivity and high random walk noise, which makes it challenging to use them directly for dead reckoning. Regular model-based inertial navigation methods require accurate modeling of IMU noise to get better results, while learning-based methods need plentiful datasets. In this article, a novel low-cost IMU dead-reckoning algorithm for wheeled mobile robot is introduced by integrating model-based and learning-based approaches, which inherits the merits of both methods. It achieves the dead reckoning by using invariant extended Kalman filter (InEKF) and IMU error model, and computes the noise parameters of the model with the aid of a deep-learning-based method. Our deep-learning-based strategy is designed to obtain noise-reduced inertial information of robots from low-cost IMU data such that the InEKF can converge. The experimental results show that the proposed method can accurately estimate the attitude, velocity, and position of the wheeled mobile robot, and can compete with vision algorithms. In addition, our proposed method consumes few computational resources to satisfy the needs of low-cost applications.