Algorithmic study of multi-sensor fusion-based vehicle navigation: comparative analysis of Kalman filter and machine learning models

The purpose of this study is to investigate the development and evaluation of a high integrity navigation system for vehicular applications, focusing on the fusion of Global Positioning System (GPS) and Inertial Measurement Unit (IMU) data. The study compares the similarities and differences in performance between the Kalman filter (a traditional GPS/IMU integration method) and machine learning models. The experiments are based on KITTI GPS/IMU sequences, and the impact of these methods on the performance of the navigation system is evaluated by introducing different noise levels. First, a Kalman filter is used to fuse the GPS/IMU data and the estimation error of the trajectories is investigated by adjusting the noise level. Second, a machine learning model is introduced to compare its performance under different parameter configurations using random forest regression as an example. In addition, the effects of different parameters on the performance of the two methods are analyzed, which provides an important reference for choosing a suitable navigation system. The results show that the Kalman filter model basically outperforms the machine learning model in terms of mean square error (MSE) and mean absolute error (MAE) of trajectory estimation. However, the random forest regression model performs best after tuning. The paper concludes with a comparison between the Kalman filter and the random forest regression model, emphasizing the robustness and adaptability of the random forest regression model in GPS/IMU fusion. The research results provide insights for the selection and design of navigation systems.