Intelligent Machine Vision System Based on Multi-sensor Integration for Ground-based Unmanned Vehicle

When developing autonomous ground transportation systems, ensuring the accuracy and reliability of machine vision is crucial for the safety of unmanned ground vehicles (UGVs). This article explores the process of integrating data from video cameras, LiDARs, and radars to create precise models of objects and their surrounding environment. The proposed algorithm consists of two stages of data fusion, which enhance the efficiency of road situation analysis, minimize errors, and improve the system’s adaptability to changing conditions.