WANG H X, YANG Y P, WANG JY, ,et al.Dynamic V-SLAM based on YOLOv5 combined with Multi-view Geometry[J].Journal of Henan Polytechnic University(Natural Science) ,2024,43(6):129-138.

doi:10.16186/j.cnki.1673-9787.2023070027

Received:2023/07/18

Revised:2024/02/26

Published:2024-09-24

Dynamic V-SLAM based on YOLOv5 combined with Multi-view Geometry

WANG Hongxing1, YANG Yaping1, WANG Jingyuan2, ZHANG Boyang3

1.School of Physics and Electronic Information Engineering，Henan Polytechnic University，Jiaozuo  454000，Henan，China；2.Miami College，Henan University，Kaifeng  475004，Henan，China；3.School of Civil Engineering，Henan Polytechnic University，Jiaozuo  454000，Henan，China

Abstract: Objectives In order to solve the problem that the traditional visual SLAM （simultaneous localization 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 module was added to the front end of visual SLAM system.This module used YOLOv5，an object detection network 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 keyframes 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 dynamic 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 efficiency 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.

Key words:V-SLAM;YOLOv5;Multi-view Geometry;PROSAC;dynamic scene