基于改进特征描述的SLAM动态算法研究

SLAM Dynamic Algorithm Based on Improved Feature Description

针对原ORB描述符算法匹配精度低、匹配耗时长,动态场景中移动的物体严重影响视觉SLAM系统的定位精度和鲁棒性,以及ORB-SLAM3系统只能构建稀疏点云地图,无法构建稠密地图的问题,提出一种基于BEBLID描述符和目标检测的改进型ORB-SLAM3。在跟踪线程中融合轻量级YOLOv5s动态目标检测网络和动态特征剔除模块,提高系统的定位精度;利用增强高效局部图像描述符BEBLID代替原特征描述算法,与原ORB特征提取方法结合,增强图像的表现力和描述效率,提升特征匹配精度和效率;增加稠密建图线程,根据关键帧与相应位姿完成稠密点云地图的构建。在公开TUM RGB-D数据集上的实验表明,与原ORB-SLAM3相比,本文算法特征匹配精度提高了7%以上;在高动态环境下系统定位精度提高98%以上,在低动态环境下最大提升60%以上,有效提高了系统在动态环境下的定位精度和鲁棒性;并构建了三维稠密点云地图,为后续应用于机器人自主导航、避障和路径规划等工作奠定了基础。

The original ORB descriptor algorithm has a low matching accuracy and long matching time,the positioning accuracy and robustness of the SLAM(simultaneous localization and mapping)system are severely disturbed by moving objects in dynamic scenes,and the ORB-SLAM3 system is incapable of constructing dense maps.To address the above problems,this paper proposes an improved ORB-SLAM3 based on the BEBLID descriptor and object detection.A lightweight YOLOv5s dynamic object detection network and dynamic feature removal module are fused with the tracking thread to improve the system's positioning accuracy.Replacing the original feature description algorithm,an improved local image descriptor BEBLID with higher efficacy is combined with the original ORB feature extraction method to enhance the expressiveness and description efficiency of images,ensuring a more accurate and efficient feature matching.A dense mapping thread is added to construct dense point cloud maps based on keyframes and correspondingposes.Experiments on the publicly available TUM RGB-D dataset show that compared with the original ORB-SLAM3,the proposed algorithm has a 7%higher feature matching accuracy;the system's positioning accuracy is improved by more than 98%in high dynamic environments and by up to 60%in low dynamic environments,showing a better positioning function in dynamic environments;a three-dimensional dense point cloud map is constructed,laying a foundation for future applications in robot autonomous navigation,obstacle avoidance,and path planning.