This paper explores the application of multi-sensor fusion technology in in-vehicle navigation systems, focusing on improving positioning accuracy, reliability, and robustness in complex environments. By integrating data from various sensors such as GPS, INS, LiDAR, and cameras, multi-sensor fusion overcomes the limitations of individual sensors, such as signal blockage and cumulative errors. The paper reviews common fusion methods, including Kalman filters, particle filters, and deep learning techniques, and presents the design and implementation of a multi-sensor fusion system. Experimental results demonstrate significant improvements in navigation performance, especially in challenging environments like urban canyons and tunnels. However, challenges remain, including the need for precise sensor calibration, the quality of the sensors, and the computational complexity of real-time data fusion. Future research should focus on optimizing fusion algorithms, improving real-time performance through hardware acceleration, and reducing system costs. The integration of emerging technologies such as V2X (vehicle-to-everything) communication and machine learning could further enhance the system’s accuracy and reliability. These advancements will play a crucial role in the future of autonomous driving, smart transportation systems, and other related fields, pushing the development of intelligent navigation technologies forward.