基于YOLOv5与多视图几何联合的动态V-SLAMOA北大核心CSTPCD

Objectives In order to solve the problem that the traditional visual SLAM(simultaneous localiza-tion and mapping)system is easily disturbed by moving objects in dynamic environment and cannot achieve accurate localization and mapping,Methods based on the ORB_SLAM2 algorithm,a dynamic V-SLAM algorithm based on YOLOv5 and Multi-view Geometry was proposed.Firstly,an object detection mod-ule was added to the front end of visual SLAM system.This module used YOLOv5,an object detection net-work in deep learning,combined with Multi-view Geometry method to identify and segment dynamic and static objects.Secondly,based on the detection results of the object detection module,in the tracking thread of the system,dynamic feature points were discarded,and only static feature points were used for inter-frame matching and pose estimation.Additionally,the progressive sample consensus(PROSAC)algorithm was employed to eliminate misaligned feature points and obtain the pose estimation model.Finally,the key-frames with dynamic information removed were used to complete the construction of a dense point cloud map.To evaluate the effectiveness of the improved algorithm,experiments were primarily conducted on dy-namic scenes from the Technical University of Munich dataset in Germany.Results The results showed that,in the experiments of image feature matching,the proposed algorithm had higher computational effi-ciency ompared with ORB feature coarse matching and random sample consensus(RANSAC)algorithm.In the trajectory tracking experiments,the proposed algorithm improved the positioning accuracy by an average of 96.14%compared with the ORB_SLAM2 system,and by an average of 94.52%compared with the ORB_SLAM3 system.In the point cloud mapping experiments,the proposed algorithm was able to construct globally consistent dense point cloud maps in three different dynamic scenes.Conclusions The improved V-SLAM algorithm had high reliability and accuracy in indoor dynamic scenarios.