IMU-Based Automated Vehicle Slip Angle and Attitude Estimation Aided by Vehicle Dynamics

Abstract

The slip angle and attitude are vital for automated driving. In this paper, a systematic inertial measurement unit (IMU)-based vehicle slip angle and attitude estimation method aided by vehicle dynamics is proposed. This method can estimate the slip angle and attitude simultaneously and autonomously. With accurate attitude, the slip angle can be estimated precisely even though the vehicle dynamic model (VDM)-based velocity estimator diverges for a short time. First, the longitudinal velocity, pitch angle, lateral velocity, and roll angle were estimated by two estimators based on VDM considering the lever arm between the IMU and rotation center. When this information was in high fidelity, it was applied to aid the IMU-based slip angle and attitude estimators to eliminate the accumulated error correctly. Since there is a time delay in detecting the abnormal estimation results from VDM-based estimators during critical steering, a novel delay estimation and prediction structure was proposed to avoid the outlier feedback from vehicle dynamics estimators for the IMU-based slip angle and attitude estimators. Finally, the proposed estimation method was validated under large lateral excitation experimental tests including double lane change (DLC) and slalom maneuvers.

A Novel Fuzzy-Adaptive Extended Kalman Filter for Real-Time Attitude Estimation of Mobile Robots

“…The microelectromechanical systems-based (MEMS-based) relative localization problem is a recent topic, which has been widely investigated in many areas including robotics and control [1][2][3][4][5][6][7][8], healthcare and rehabilitation [9][10][11], consumer electronics mobile devices [12][13][14], and automated driving and navigation [15][16][17][18], both in industry and in scientific research. Independent from the application, accurate and robust attitude estimation is a crucial task to be solved, especially if the results are to be incorporated into unstable closed-loop systems, such as the control algorithms of mobile robots and unmanned aerial vehicles (UAVs) [1].…”

“…The estimation performance of EKF is mostly determined by the noise covariance matrices Q and R. Unfortunatelly, in practice, these parameters (i.e., the statistical description of the state and observation noises) are not fully measurable (or require time consuming, complex, and extensive verification and validation procedures); especially in the case of MARG sensors, as the effects of both different noise sources and disturbances are represented with general noise vectors v k and w k in Equations (16) and (17). Generally, the parameters Q and R are tuned based on engineering intuition through trial-and-error analysis; however, that method yields only a compromise solution between the estimation accuracy and filter dynamics.…”

Accelerometer-Based Gyroscope Drift Compensation Approach in a Dual-Axial Stabilization Platform

“…ω zp and ω xp are measured by gyro sensor, which is installed in pitch frame. Substituting Equations (4), (7) and (9) into Equation (15) and (16). Compensation angular velocity in pitch and heading frame are obtained and represented as follows [14].…”

“…At this time, the output of y acc axis accelerometer is not zero and assume it is y a . Pitch angle is represented as follows by using trigonometrical function [16].…”

An Acquisition Method of Agricultural Equipment Roll Angle Based on Multi-Source Information Fusion

“…The roll angle of agricultural machine can be measured in multiple ways [5][6][7][8][9], and the commonly used method is based on inertial sensors. Inertial sensors generally include accelerometers and gyroscopes [10][11][12][13] or inclination sensors [14,15]. For instance, an external acceleration inclination angle Kalman filter algorithm was proposed for agricultural machinery [1].…”