Research on fusion algorithm based for multimodal sensor

GPS/IMU multi-sensor fusion algorithm is of great significance in the auto drive system. GPS has high precision, but the sampling frequency is low and prone to failure; The IMU sensor has a high sampling frequency and is relatively stable, but it is prone to error accumulation. Therefore, the two have good complementarity, and integrating them can obtain a navigation solution with better performance than a single navigation system. In recent years, many algorithms based on Kalman filters (KF) have emerged, and some scholars have proposed using artificial intelligence to fuse GPS/IMU data. This article aims to effectively fuse multimodal sensors and deeply analyzes the advantages and disadvantages of existing algorithms based on Kalman filters, machine learning algorithms, and neural networks.