A Gyroless Attitude Estimation for Ground Vehicle Under Severe Dynamic Conditions

In this paper, we propose an inertial measurement unit (IMU) based accurate attitude estimation algorithm for land vehicles using only accelerometer and magnetometer data along with a set of nonholonomic constraints on vehicle motion. Currently, most of the IMU based attitude estimation methods deploy gyroscopes along with accelerometer measurements which work efficiently only either for static or quasi-static conditions. However, during prolonged dynamic phases of vehicle motion, the accelerometer measurements are corrupted due to external acceleration which deteriorates the attitude estimation accuracy. In this paper, we illustrate that vehicle and sensor constraints allow the removal of external acceleration from accelerometer measurement in a magnetically clean environment. This, in turn, enables to design and use a linear Kalman filter (KF) using only accelerometer and magnetometer data for accurate attitude estimation. The proposed algorithm is tested and compared with three different state-of-the-art algorithms in simulation and real-world experiments under various dynamic conditions. The results illustrate that vehicle attitude information can be obtained accurately and reliably during long term fast dynamic conditions, using the designed KF.