基于YOLOv8的室内动态场景下视觉SLAM方法研究
作者:
作者单位:

1.南京信息工程大学遥感与测绘工程学院;2.南京信息工程大学软件学院

基金项目:

江苏省重点研发计划社会发展面上项目(BE2021622)


Research on Visual SLAM Method for Indoor Dynamic Scenes Based on YOLOv8
Author:
Affiliation:

1.School of Remote Sensing and Surveying engineering,Nanjing University of Information Science and Technology;2.School of Software,Nanjing University of Information Science and Technology

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    摘要:

    针对在室内动态环境中,传统视觉SLAM算法受到大量无意义信息的影响,导致定位精度下降、鲁棒性差的问题,提出一种基于YOLOv8的室内动态场景视觉SLAM——PLYO-SLAM算法。该算法在ORB-SLAM3算法跟踪线程引入ED Lines线段检测算法,并新增动态区域检测线程。动态区域检测线程由YOLOv8n-seg实例分割网络组成,实例分割赋予动态场景语义信息并生成动态掩码,同时剔除动态区域点线特征,利用几何约束进一步过滤分割掩码外缺失的动态点特征。使用公开数据集TUM进行实验验证,结果表明,相较于ORB-SLAM3算法,PLYO-SLAM算法在动态环境下的定位精度平均提高了75.99%,最高达到96.75%。

    Abstract:

    Aiming at the problem that in indoor dynamic environments, traditional visual SLAM algorithms are affected by a large amount of meaningless information, which leads to a decrease in localization accuracy and poor robustness, a visual SLAM algorithm PLYO-SLAM for indoor dynamic scenes based on YOLOv8 is proposed. In this paper, the algorithm introduces EDLines line segment detection algorithm in the tracking thread of ORB-SLAM3 algorithm, and adds a new Dynamic region detection thread. The dynamic region detection thread consists of a YOLOv8n-seg instance segmentation network, where the instance segmentation empowers the dynamic scene semantic information and generates dynamic masks, and at the same time eliminates the point and line features of dynamic regions, and utilizes the geometric constraints to further filter the missing dynamic feature points outside the segmentation masks. Experimental validation using the publicly available dataset TUM shows that compared to the ORB-SLAM3 algorithm, the PLYO-SLAM algorithm improves the localization accuracy in dynamic environments by an average of 75.99% and reaches a maximum of 96.75%.

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黄钰洲,柯福阳.基于YOLOv8的室内动态场景下视觉SLAM方法研究[J].南京信息工程大学学报,,():

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  • 收稿日期:2024-06-26
  • 最后修改日期:2024-09-28
  • 录用日期:2024-09-30

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