测绘通报 ›› 2025, Vol. 0 ›› Issue (4): 14-19,26.doi: 10.13474/j.cnki.11-2246.2025.0403

• 人工智能与视觉系统 • 上一篇    

基于多特征信息定位的机器人视觉SLAM算法

范启亮1, 丁度坤1,2   

  1. 1. 东莞职业技术学院电子信息学院, 广东 东莞 523808;
    2. 广西大学广西制造系统与先进制造技术重点实验室, 广西 南宁 530004
  • 收稿日期:2024-12-03 发布日期:2025-04-28
  • 作者简介:范启亮(1984—),男,硕士,讲师,主要研究方向为工业机器人技术、自动化。E-mail:fql198405@163.com
  • 基金资助:
    广东省教育科学规划课题(2021GXJK601);广东省高职院校产教融合创新平台项目(2020CJPT014);东莞职业技术学院横向科研项目(2020H182)

Robot visual SLAM algorithm based on multi-feature information localization

FAN Qiliang1, DING Dukun1,2   

  1. 1. School of Electronic Information, Dongguan Polytechnic, Dongguan 523808, China;
    2. Guangxi Key Lab of Manufacturing System & Advanced Manufacturing Technology, Guangxi University, Nanning 530004, China
  • Received:2024-12-03 Published:2025-04-28

摘要: 视觉即时定位与地图构建(SLAM)算法在室内服务机器人中被广泛应用,但基于点云、平面和语义的视觉SLAM算法存在地图构建单一、定位不准等问题。本文基于经典ORB-SLAM2算法,引入平面和语义信息,提出基于多特征信息定位的视觉SLAM算法(MFIL-SLAM)。该算法通过从视觉和深度图像中提取特征点、平面和语义信息,与地图中的相应路标关联,更新相机位姿,并通过因子图优化多层级地图。试验结果表明,本文算法在建图效果、定位精度和稳健性方面均优于现有算法。

关键词: 多特征信息, 视觉SLAM, 数据关联, 因子图优化

Abstract: The visual simultaneous localization and mapping (SLAM) algorithm has been widely applied in indoor service robots. However, current point cloud, plane-based, and semantic visual SLAM algorithms face issues such as single map structures and inaccurate localization. This paper proposes a multi-layered map construction SLAM (MFIL-SLAM)algorithm based on the classical ORB-SLAM2, incorporating plane and semantic information. The algorithm extracts feature points, planes, and semantic objects from visual and depth images, associates them with map landmarks, updates camera poses, and optimizes multi-layered maps through factor graph optimization. Experimental results demonstrate that the proposed algorithm outperforms existing ones in mapping quality, localization accuracy, and robustness.

Key words: multi-feature information, visual SLAM, data association, factor graph optimization

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