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<p>Under complex lighting conditions, hospital logistics robots are facing serious challenges in positioning and navigation.The traditional ORB (Oriented FAST and rotated BRIEF) algorithm often has problems such as unstable feature point extraction, poor positioning accuracy, and long navigation path planning time in environments with large lighting changes, which greatly affects the robot's navigation efficiency and accuracy. In this paper, an improved ORB-SLAM3 algorithm is used to improve the navigation performance of the robot in a hospital environment with complex lighting. The improved ORB-SLAM3 algorithm extracts feature points of different scales by constructing an image pyramid to ensure that effective visual information can be stably obtained under different lighting conditions; the strategy of combining adaptive threshold and dual threshold methods is used to optimize the accuracy of feature point extraction, and the feature points are efficiently managed through a four-tree model to ensure the uniform distribution of feature points. These measures significantly improve the quality and matching accuracy of feature point extraction; the algorithm also combines visual and inertial information closely by combining inertial measurement unit data, further enhancing the positioning accuracy and system stability.The experimental results show that in the environment of rapid light changes, the average positioning error, navigation map construction integrity and navigation planning time of the improved ORB-SLAM3 algorithm are 0.26&#xa0;m, 80% and 7.26&#xa0;ms, respectively.This achievement not only effectively improves the navigation efficiency of the hospital logistics robot, but also enhances its stability and reliability in an environment with severe lighting changes. It is of great practical significance to improve the level of intelligence of the hospital's internal logistics system.</p>

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Improving the navigation optimization of hospital logistics robots under complex lighting changes by using improved ORB-SLAM3 and deep learning visual SLAM algorithm

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月2日

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  • Feng Xiao,
  • Youhai Zhang,
  • Jie Fang,
  • Xing Guo,
  • Rubing Huang

摘要

Under complex lighting conditions, hospital logistics robots are facing serious challenges in positioning and navigation.The traditional ORB (Oriented FAST and rotated BRIEF) algorithm often has problems such as unstable feature point extraction, poor positioning accuracy, and long navigation path planning time in environments with large lighting changes, which greatly affects the robot's navigation efficiency and accuracy. In this paper, an improved ORB-SLAM3 algorithm is used to improve the navigation performance of the robot in a hospital environment with complex lighting. The improved ORB-SLAM3 algorithm extracts feature points of different scales by constructing an image pyramid to ensure that effective visual information can be stably obtained under different lighting conditions; the strategy of combining adaptive threshold and dual threshold methods is used to optimize the accuracy of feature point extraction, and the feature points are efficiently managed through a four-tree model to ensure the uniform distribution of feature points. These measures significantly improve the quality and matching accuracy of feature point extraction; the algorithm also combines visual and inertial information closely by combining inertial measurement unit data, further enhancing the positioning accuracy and system stability.The experimental results show that in the environment of rapid light changes, the average positioning error, navigation map construction integrity and navigation planning time of the improved ORB-SLAM3 algorithm are 0.26 m, 80% and 7.26 ms, respectively.This achievement not only effectively improves the navigation efficiency of the hospital logistics robot, but also enhances its stability and reliability in an environment with severe lighting changes. It is of great practical significance to improve the level of intelligence of the hospital's internal logistics system.