A Review of Multi-Sensor Fusion System for Large Heavy Vehicles Off Road in Industrial Environments

De Jong Yeong, John Barry, Joseph Walsh
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引用次数: 10

Abstract

Industry 4.0 or fourth industrial revolution elevates the computerization of Industry 3.0 and enhances it with smart and autonomous systems driven by data and Machine Learning. This paper reviews the advantages and disadvantages of sensors and the architecture of multi-sensor setup for object detection. Here we consider the case of autonomous systems in for large heavy vehicles off-road in industrial environments with the use of camera sensor, LiDAR sensor, and radar sensor. Understanding the vehicles surroundings is a vital task in autonomous operation where personnel and other obstacles present significant hazard of collision. This paper review further discusses the challenges of time synchronisation on sensor data acquisition in multi-modal sensor fusion for personnel and object detection, and details a solution implemented in a Python environment.
大型重型汽车工业越野多传感器融合系统研究进展
工业4.0或第四次工业革命提升了工业3.0的计算机化,并通过数据和机器学习驱动的智能和自主系统增强了它。本文综述了传感器的优点和缺点,以及用于目标检测的多传感器装置的结构。在这里,我们考虑在工业环境中使用相机传感器、激光雷达传感器和雷达传感器的大型重型车辆越野自动驾驶系统的情况。在人员和其他障碍物存在重大碰撞危险的自动驾驶中,了解车辆周围环境是一项至关重要的任务。本文进一步讨论了在人员和目标检测的多模态传感器融合中传感器数据采集的时间同步挑战,并详细介绍了在Python环境中实现的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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