Deep Learning-Based Multi-Modal Fusion for Robust Robot Perception and Navigation

-- This paper introduces a novel deep learning - based multimodal fusion architecture aimed at enhancing the perception capabilities of autonomous navigation robots in complex environments. The key contributions of this work are as follows: a. the design of a lightweight feature extraction network to enhance feature representati on; b. the development of an adaptive weighted cross - modal fusion strategy to improve system robustness; and c. the incorporation of time - series information modeling to boost dynamic scene perception accuracy. Experimental results on the KITTI dataset demonstrate that the proposed approach increases navigation and positioning accuracy by 3.5% and 2.2%, respectively, while maintaining real - time performance. This work provides a novel solution for autonomous robot navigation in complex environments. With the rapid development of robotics, autonomous navigation capability has become a core requirement for robotic systems. In practical applications, robots need to accurately perceive and reliably navigate in dynamically changing environments.