Extended Kalman filter fusion model for localization

To improve the accuracy of autonomous driving, a fusion algorithm is needed for vehicle localization. I propose the Extended Kalman filter fusion model, which has been experimentally proven to be more powerful. The paper compares three algorithms, namely linear regression, decision tree, and Kalman filtering, on their effectiveness in fusing GPS and IMU sensor data for prediction using the KITTI dataset. The results show that Kalman filtering provides the best prediction performance. Additionally, the article evaluates the noise resistance capabilities of the Kalman filter and finds that it performs well in dealing with noise. However, I note that Kalman filtering still needs improvement in dealing with highly nonlinear systems and determining noise covariance. To address these issues, it is recommended modifying the values of variance, noise covariance, state covariance, and dt. The paper briefly introduces the Unscented Kalman filter and proposes possible different solutions and ideas. In conclusion, experts, policymakers, and the general public need to continue exploring and developing better solutions to the vehicle localization problem in the rapidly developing field of autonomous driving.