针对室内高低动态环境的视觉SLAM算法研究  

Research on visual SLAM algorithm for indoor high and low dynamic environments

作　　者：符强[1,2] 曾凡治 纪元法[1,2,3] 任风华 Fu Qiang;Zeng Fanzhi;Ji Yuanfa;Ren Fenghua(Guangxi Key Laboratory of Precision Navigation Technology and Application,Guilin University of Electronic Technology,Guilin 541004,China;School of Information and Communication,Guilin University of Electronic Technology,Guilin 541004,China;International Joint Laboratory of Spatiotemporal Information and Intelligent Location Services,Guilin 541004,China)

机构地区：[1]桂林电子科技大学广西精密导航技术与应用重点实验室,桂林541004 [2]桂林电子科技大学信息与通信学院,桂林541004 [3]时空信息与智能位置服务国际联合实验室,桂林541004

出　　处：《电子测量技术》2024年第21期159-167,共9页Electronic Measurement Technology

基　　金：国家自然科学基金(U23A20280,62161007,62061010);广西科技厅项目(桂科AD22080061,桂科AA23062038,桂科AB23026120);广西精密导航与应用重点实验室基金(DH202308)项目资助。

摘　　要：针对大多数经典视觉SLAM在室内的动态环境下鲁棒性不足问题,在基于ORB-SLAM3算法框架之下,提出了一种可区分室内高低动态环境的视觉SLAM。首先提出一种根据连续多帧之间位姿变换做重投影误差来区分室内环境中的先验动态对象处于高动态还是低动态的算法。然后根据环境的高低动态决定是否结合YOLOv8-Seg实例分割网络对动态环境中的动态特征进行剔除,保证SLAM系统的跟踪精度。最后针对动态特征引起地图中出现重复性的地图点,在局部地图跟踪加入一种重复地图点消除算法,对动态环境中出现的重复地图点进行删除,进一步保证系统的稳定跟踪。在公开数据集TUM RGB-D上实验结果表明,改进后的算法相对于ORB-SLAM3算法在定位精度上均有提升,低动态环境下最大提升60.41%,高动态环境下最大提升94.65%。与其他动态特征去除算法相比,在大部分序列上实现了更高的定位精度,且在实时性上也更具优势。在所提算法有效解决SLAM应对室内动态环境的问题,提升了SLAM的定位精度。Aiming at the problem that most classic visual SLAMs are not robust enough in indoor dynamic environments,a visual SLAM that can distinguish between high and low dynamic environments is proposed based on the ORB-SLAM3 algorithm framework.First,an algorithm is proposed to distinguish whether the prior dynamic objects in indoor environments are in high or low dynamics based on the reprojection error of the pose transformation between multiple consecutive frames.Then,according to the high and low dynamics of the environment,it is decided whether to combine the YOLOv8-Seg instance segmentation network to remove the dynamic features in the dynamic environment to ensure the tracking accuracy of the SLAM system.Finally,in order to deal with the repeated map points in the map caused by dynamic features,a repeated map point elimination algorithm is added to the local map tracking to delete the repeated map points in the dynamic environment,further ensuring the stable tracking of the system.Experimental results on the public dataset TUM RGB-D show that the improved algorithm has improved the positioning accuracy compared with the ORB-SLAM3 algorithm,with a maximum improvement of 60.41%in low dynamic environments and a maximum improvement of 94.65%in high dynamic environments.Compared with other dynamic feature removal algorithms,higher positioning accuracy is achieved in most sequences,and it is also more advantageous in real-time performance.The proposed algorithm effectively solves the problem of SLAM coping with indoor dynamic environments and improves the positioning accuracy of SLAM.

关 键 词：同时定位与建图 ORB-SLAM3 YOLOv8-Seg 高低动态环境 重复地图点消除 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术] TN98[自动化与计算机技术—计算机科学与技术]