结合轻量化YOLOv5s的动态视觉SLAM算法
Dynamic visual SLAM algorithm combined with lightweight YOLOv5s
黄友锐 1王照锋 2韩涛 1宋红萍1
作者信息
- 1. 安徽理工大学电气与信息工程学院 淮南 232001
- 2. 安徽理工大学计算机科学与工程学院 淮南 232001
- 折叠
摘要
针对视觉SLAM在真实环境中易受车辆、行人等运动物体影响而导致位姿估计降低的问题,提出一种结合轻量化YOLOv5s的动态视觉SLAM算法,将改进的轻量级YOLOv5s作为目标检测算法用于判断运动物体;结合提出的动态特征点剔除方法,剔除动态特征点,仅采用静态特征点进行位姿估计和地图跟踪.在TUM数据集进行实验,相较于ORB-SLAM3算法,改进后的算法在高动态序列上的位姿估计精度分别提升了89.29％、65.34％、94.42％,结果表明改进后的算法能够有效剔除动态特征点,提高了视觉SLAM算法在动态环境下的位姿估计精度和定位精度.
Abstract
Aiming at the problem that visual SLAM is easily affected by moving objects such as vehicles and pedestrians in real environment,resulting in low pose estimation accuracy,a dynamic visual SLAM algorithm combined with lightweight YOLOv5s is proposed.The improved lightweight YOLOv5s is used as an object detection algorithm to judge moving objects.Combined with the proposed method of dynamic feature points elimination,dynamic feature points are eliminated,and only static feature points are used for pose estimation and map tracking.Experiments on TUM data set show that compared with ORB-SLAM3 algorithm,the pose estimation accuracy of the improved algorithm on high dynamic sequence is improved by 89.29％,65.34％ and 94.42％ respectively.The results show that the improved algorithm can effectively eliminate dynamic feature points.The pose estimation and positioning accuracy of visual SLAM algorithm in dynamic environment are improved.
关键词
动态SLAM/目标检测/光流法/ORB特征点Key words
dynamic SLAM/object detection/LK optical flow/ORB feature point引用本文复制引用
基金项目
安徽省高效协同创新项目(GXXT-2023-068)
出版年
2024
NETL
NSTL
万方数据