@article {2847, title = {An Attitude Estimation Algorithm for Mobile Robots under Unknown Magnetic Disturbances}, journal = {IEEE/ASME TRANSACTIONS ON MECHATRONICS}, volume = {21}, year = {2016}, pages = {1900-1911}, chapter = {1900}, abstract = {
Attitude estimation is a crucial aspect for navigation and motion control of autonomous vehicles. This concept is particularly true in the case of unavailability of localization sensors when navigation and control rely on dead reckoning strategies; in this case, indeed, the orientation estimate is also used along with speed measurements to update the position estimate. Among the different approaches proposed in the literature, the de facto state of the art in this field is represented by nonlinear complementary filters: they fuse the measurements of angular rate obtained through gyroscopes, and a measurement of gravity and Earth{\textquoteright}s magnetic field vectors respectively obtained through accelerometers and magnetometers. This paper is focused on an attitude estimation strategy for autonomous underwater vehicles (AUV). The proposed novelty includes the identification of some critical issues that arise when AUV attitude estimation algorithms are applied in practice.
They are mainly due to the use of low-accuracy low-cost microelectromechanical systems (MEMS) sensors and on different sources of magnetic disturbances. Some strategies to overcome the identified issues are proposed, including the integration of a single-axis fiber optic gyroscope (FOG) that ensures a considerable performance improvement with a moderate cost increase. The proposed strategies for detection of issues and sensor fusion have been experimentally tested and validated in a real application scenario estimating the attitude of an AUV performing a lawn mower path.
The expected performance improvement is confirmed; the obtained results are described and analyzed in this paper.
}, keywords = {Robotics}, doi = {10.1109/TMECH.2016.2559941}, url = {http://ieeexplore.ieee.org/document/7460980/}, author = {R. Costanzi and F. Fanelli and N. Monni and A. Ridolfi and B. Allotta} }