Abstract
This article discusses the evolution of vehicle navigation systems, highlighting the limitations of traditional GPS systems, especially in challenging environments such as urban canyons or tunnels. Sensor fusion, the integration of data from multiple sensors such as LiDAR, cameras, and inertial measurement units (IMUs), is a powerful alternative. By combining the advantages of these different sensors, sensor fusion improves the accuracy, reliability, and safety of vehicle navigation, especially for autonomous vehicles and advanced driver assistance systems (ADAS). The study suggests various sensors, data integration algorithms, and potential improvements to navigation systems. The goal of multi-sensor fusion in-vehicle navigation is to combine data collected from a set of sensors to improve the quality of the solution. Different types of sensors provide physical properties. Different fusions are used to directly integrate sensor data to obtain parameters, while other fusions are used to indirectly integrate sensor data in a layered vehicle to obtain control signals suggested by commands from different modules.
Published Version
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