Robust Sensor Fusion for Robot Attitude Estimation
This provides a robust attitude estimation solution for robotics applications, though it appears incremental as it builds on existing nonlinear complementary filter methods.
The paper tackles the problem of estimating robot orientation by fusing gyroscope, accelerometer, and magnetometer data into a quaternion estimate, introducing the concept of fused yaw and achieving uniform robustness and stability across all orientations.
Knowledge of how a body is oriented relative to the world is frequently invaluable information in the field of robotics. An attitude estimator that fuses 3-axis gyroscope, accelerometer and magnetometer data into a quaternion orientation estimate is presented in this paper. The concept of fused yaw, used by the estimator, is also introduced. The estimator, a nonlinear complementary filter at heart, is designed to be uniformly robust and stable---independent of the absolute orientation of the body---and has been implemented and released as a cross-platform open source C++ library. Extensions to the estimator, such as quick learning and the ability to deal dynamically with cases of reduced sensory information, are also presented.