Comparative Study on the Robustness against Gyroscope Bias of Rule-based and Machine-Learning-based Attitude Estimation Approaches

Abstract

Attitude estimation based on a six-axis inertial measurement unit (IMU) consisting of an accelerometer and a gyroscopeis a fundamental technology utilized in various applications. Accordingly, IMU-based attitude estimation methods have beenwidely studied using rule-based algorithms and machine learning (ML) techniques. Among the factors contributing to estimationerrors, the bias inherent in the gyroscope signals is particularly significant. Although numerous studies have focused on bias compensationmethods, studies on the robustness of attitude estimation methods against gyroscope biases of various magnitudes arescarce. Therefore, this study aimed to compare and analyze the robustness of rule-based and ML-based attitude estimation methodsagainst different magnitudes of gyroscopic bias. In this study, higher robustness was defined as lower performance degradation. Thebias compensation mechanism exhibited outstanding performance in compensating for constant biases, leading to a substantial differencein the estimation performance of rule-based algorithms depending on the presence of bias compensation. In addition, biasaugmentation did not enhance the robustness of ML-based methods. Finally, rule-based algorithms incorporating bias compensationmechanisms exhibited superior robustness against biases compared with ML-based methods.