Széchenyi Plusz RRF

In the motion estimation of self-driving vehicles, the three main requirements are accuracy, robustness, and cost-effectiveness. The generally applied sensors and methods are the GNSS, inertial, and visual-odometry, but the contradictory requirements demand the integration of new ideas. The wheel odometry could be an adequate choice since the method is robust and cost-effective, but the accuracy of the estimation is limited by the parameter uncertainty, thus a calibration method should be included as well. However, the general parameter identification of a nonlinear model in the presence of noise has not been solved yet. The presented method is based on the assumption that noisy, but several measurements of GNSS and IMU sensors are available in a self-driving vehicle. In the proposed architecture, nonlinear least squares and optimal control techniques are combined in a unique way to compensate for the noise of the orientation and wheel rotation signals to achieve unbiased model calib ration. The performance of the developed algorithm and the accuracy of parameter estimation are demonstrated with detailed validation and a test with a real vehicle.