Research on multimodal sensor fusion algorithms

As navigation systems are increasingly used in social life, people's demands for accuracy in positioning are also rising. However, commonly used technologies such as GPS still have some limitations. For instance, GPS signals can experience attenuation and obstruction indoors, in urban canyons, and in complex environments, which limits their positioning accuracy and robustness. In addition, IMU sensors accumulate errors over time, leading to the consequence of position drift. Therefore, in order to mitigate the drawbacks of using a single sensor, enhance the reliability of positioning systems, and provide people with better services, this paper primarily investigates the fusion algorithms of multimodal sensors. It conducts experiments on various algorithms related to sensor data fusion, and based on experimental results, compares them from different perspectives to determine the optimal algorithm. The studied algorithms include Kalman filtering and machine learning. Finally, a comparison of the noise resistance was conducted for the fusion algorithm with good performance.