北京邮电大学学报

  • EI核心期刊

北京邮电大学学报 ›› 2024, Vol. 47 ›› Issue (1): 106-111.

• 论文 • 上一篇    下一篇

适应于弱光环境的 ORB-SLAM 算法

黎萍1,操超超2   

  1. 1. 电子科技大学中山学院
    2. 广东工业大学
  • 收稿日期:2022-11-14 修回日期:2023-02-23 出版日期:2024-02-26 发布日期:2024-02-26
  • 通讯作者: 黎萍 E-mail:lipingjxau@163.com
  • 基金资助:
    广东省自然科学基金项目

ORB-SLAM Algorithm for Low Light Environment

LI Ping1, CAO Chaochao2   

  • Received:2022-11-14 Revised:2023-02-23 Online:2024-02-26 Published:2024-02-26
  • Contact: Ping Li E-mail:lipingjxau@163.com

摘要: 针对视觉同步定位与地图构建(SLAM)算法在弱光照或黑暗等复杂条件下定位失败和跟踪丢失等问题,提出了一种基于 ORB-SLAM2 算法的适合弱光环境的 ORB-SLAM 算法,其中应用了一种新的自适应图像增强模块,利用多尺度高斯函数提取的输入图像照度分量,所设计的校正因子可根据照度分量进行动态调整,从而自适应地调整图像亮度。在公开数据集上进行了算法性能测试。结果表明,该算法能够有效地增强弱光照甚至黑暗等复杂条件下视觉图像的特征匹配,从而有效提升 ORB-SLAM 算法的鲁棒性。

关键词: 弱光环境, 同步定位与地图构建, 图像增强, 多尺度高斯函数, 自适应校正

Abstract: Aiming at the localization failure and tracking loss of visual simultaneous localization and map building (SLAM) in complex environments such as weak lighted or even totally dark, a vision SLAM algorithm suitable for weak light environment is proposed based on ORB-SLAM2, to which, a new adaptive image enhancement algorithm is applied. Image brightness is adapted by means of correction factor , which can be dynamically adjusted according to illuminance component of input image extracted by multi-scale Gaussian function. Performance of the algorithm is tested on public dataset. Simulation results show that the algorithm can efficiently help feature matching in complex environments such as weak lighted or even totally dark, consequently, the robustness of ORB-SLAM is improved effectually. 

Key words: low light environment, simultaneous localization and mapping, image enhancement, multi-scale Gaussian function, adaptive correction

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