基于多特征信息定位的机器人视觉SLAM算法  

Robot visual SLAM algorithm based on multi-feature information localization

作　　者：范启亮[1] 丁度坤[1,2] FAN Qiliang;DING Dukun(School of Electronic Information,Dongguan Polytechnic,Dongguan 523808,China;Guangxi Key Lab of Manufacturing System&Advanced Manufacturing Technology,Guangxi University,Nanning 530004,China)

机构地区：[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

出　　处：《测绘通报》2025年第4期14-19,26,共7页Bulletin of Surveying and Mapping

基　　金：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)。

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。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.

关 键 词：多特征信息 视觉SLAM 数据关联 因子图优化 

分 类 号：P228[天文地球—大地测量学与测量工程]