关键词: 智能网联汽车；摄像头标定；教学台架；ROS2；低成本

Abstract: With the rapid development of intelligent and connected vehicles, the teaching of their environmental perception systems has become increasingly important for professional talent cultivation. However, current teaching methods commonly face challenges such as the high cost of practical training equipment and insufficient hands-on student practice. To address this issue, this paper designs and implements a low-cost, modular teaching platform for onboard camera intrinsic and extrinsic parameter calibration. The platform employs a desktop-style hardware design, using a Raspberry Pi 5 as the core computing unit and equipped with multiple cameras. At the software level, it integrates calibration algorithms based on the robot operating system 2 (ROS2) framework,forming a complete teaching solution. Application results demonstrate that this platform can effectively reduce costs to less than 1/5 of traditional equipment, significantly mitigating the disconnect between theoretical instruction and practical training, thereby holding practical significance for improving teaching quality and popularizing key technology training.

Key words: intelligent and connected vehicles; camera calibration; teaching platform; ROS2; lowcost