Neural Network-Based Multimodal Fusion for Navigation

Abstract

This article is based on deep neural networks and explores how to effectively integrate multiple sensor data to improve navigation accuracy and environmental perception capabilities. With the popularity of sensors such as LiDAR, cameras, and inertial measurement units, various data sources provide rich information, which provides possibilities for multimodal fusion. This paper presented a new multimodal data fusion framework that combines a convolutional neural network (CNN) and a long short-term memory network (LSTM) to process spatial and temporal data features. This effectively enhances the robustness and adaptability of navigation systems in changing environments. Research has shown that multimodal fusion navigation methods based on neural networks not only improve positioning accuracy but also increase response speed by 30% in dynamic environments. In addition, by analyzing the data fusion process of different sensors, we can find that the synergy between sensors can effectively reduce the measurement error and improve the robustness of the system, especially in complex terrain and a changing environment.