Single axis FOG aided attitude estimation algorithm for mobile robots

This paper is focused on an attitude estimation method for Autonomous Underwater Vehicles (AUVs). Data acquired by a commercial Micro-Electro-Mechanical Systems (MEMS) Inertial Measurement Unit (IMU), equipped with magnetometers, and a Fibre Optic Gyroscope (FOG) are fused to estimate the attitude of the vehicle. One of the most used attitude estimation filter, a Nonlinear Complementary Filter (NCF), is proposed as the basis of this work; then, some adaptations to the original formulation of the filter are illustrated to better suit it to the field of underwater robotics. The proposed improvements include the online tuning of the gains of the filter to cope with sensor disturbances and the employment of the data acquired by a FOG. In addition, a fast procedure for the calibration of a magnetometer is introduced to increase the reliability of its readings. The resulting filter is used to estimate the attitude of an AUV; the performances of the proposed solution are tested and evaluated, in particular when unpredictable magnetic disturbances are present, highlighting the improvements that the applied changes allow to achieve in the specific field of application.