现代制造工程 ›› 2026, Vol. 547 ›› Issue (4): 129-136.doi: 10.16731/j.cnki.1671-3133.2026.04.015

• 仪器仪表/检测/监控 • 上一篇    下一篇

基于ROS和ORB-SLAM3算法的智能行车系统研究*

涂福泉1, 肖雪亭1, 黄煜2, 刘祥3, 闾丰翼1   

  1. 1 武汉科技大学冶金装备及其控制教育部重点实验室,武汉 430081;
    2 宝武环科武汉金属资源有限责任公司,武汉 430080;
    3 北湖机电工程有限公司,武汉 430080
  • 收稿日期:2025-04-15 发布日期:2026-05-07
  • 通讯作者: 肖雪亭,硕士研究生,主要研究方向为机器人运动控制。E-mail:1172404338@qq.com
  • 作者简介:涂福泉,博士,教授,主要研究方向为智能材料驱动器与传感器、机器人运动控制。E-mail:493132199@qq.com
  • 基金资助:
    *国家自然科学基金项目(52375061)

Research on intelligent crane system based on ROS and ORB-SLAM3 algorithm

TU Fuquan1, XIAO Xueting1, HUANG Yu2, LIU Xiang3, LÜ Fengyi1   

  1. 1 Key Laboratory of Metallurgical Equipment and Control,Ministry of Education,Wuhan University of Science and Technology,Wuhan 430081,China;
    2 Baowu Huanke Wuhan Metal Resources Co.,Ltd.,Wuhan 430080,China;
    3 North Lake Mechanical and Electrical Engineering Co.,Ltd.,Wuhan 430080,China
  • Received:2025-04-15 Published:2026-05-07

摘要: 针对现有智能行车系统在工业场景中面临的部署复杂度高、可移植性差的技术瓶颈,提出了一种基于机器人操作系统(Robot Operating System,ROS)和ORB-SLAM3算法的智能行车系统。通过功能分析,构建了智能行车系统总体架构,首先,使用深度相机实时采集料堆的环境信息;然后,对ORB-SLAM3算法进行改进,增加了稠密建图线程与安全取料线程,实现行车实时定位、料库区三维重建及目标取料点规划;最后,基于ROS的标准化运动控制接口,确保行车稳定可靠运行。模拟实验表明,智能行车系统定位与三维重建精度高,可实时生成安全高效的取料策略;ROS分布式架构与深度相机的快速部署,降低了部署复杂度,提升了智能行车系统的可移植性。研究结果有望为解决行业现存的部署复杂度高和可移植性差的问题提供切实可行的技术路径。

关键词: 智能行车系统, 机器人操作系统, ORB-SLAM3算法, 深度相机

Abstract: In view of the technical bottlenecks of high deployment complexity and poor portability faced by existing intelligent crane systems in industrial scenarios, this study proposes an intelligent crane system based on the Robot Operating System (ROS) and the ORB-SLAM3 algorithm. Through functional analysis, the overall architecture of the intelligent crane system is constructed. Specifically, a depth camera is first utilized to capture real-time environmental information of material piles. Subsequently, the ORB-SLAM3 algorithm is optimized by incorporating a dense mapping thread and a safe material-picking thread, thereby enabling real-time crane positioning, three-dimensional reconstruction of the material storage area, and planning of target material-picking points. Finally, by leveraging the standardized motion control interface of ROS, the stable and reliable operation of the crane is ensured. Simulation experiments demonstrate that the proposed intelligent crane system achieves high accuracy in positioning and three-dimensional reconstruction, and is capable of generating safe and efficient material-picking strategies in real time. Furthermore, the distributed architecture of ROS and the rapid deployment of the depth camera contribute to reducing deployment complexity and enhancing the portability of the intelligent crane system. The research findings are anticipated to provide a feasible technical approach for addressing the long-standing issues of high deployment complexity and poor portability within the industry.

Key words: intelligent driving system, Robot Operating System (ROS), ORB-SLAM3 algorithm, depth camera

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