WANG H X, XU W L, ZHANG B Y.V-SLAM algorithm based on improved ORB and PROSAC feature point matching[J].Journal of Henan Polytechnic University(Natural Science) ,2023,42(1):152-159.

doi:10.16186/j.cnki.1673-9787.2021040137

Received:2021/04/30

Revised:2021/06/09

Published:2023/01/25

V-SLAM algorithm based on improved ORB and PROSAC feature point matching

WANG Hongxing1, XU Wanlin1, ZHANG Boyang2

1.School of Physics and Electronic Information，Henan Polytechnic University，Jiaozuo  454000，Henan，China；2.School of Civil Engineering，Henan Polytechnic University，Jiaozuo  454000，Henan，China

Abstract:With the rapid development of machine vision，visual simultaneous localization and mapping （V-SLAM） has become a research hotspot of indoor positioning and navigation applications.Aiming at the non-uniform distribution of feature points extracted by the traditional ORB algorithm，the quadtree algorithm was used to uniformly distribute feature points at the front-end，and progressive sample consensus （PROSAC） algorithm was used to eliminate mismatched feature points.At the back-end，bag of words （BoW） was used to detect loop closure in the key frames and the bundle adjustment （BA） was used to correct the camera position-posture data.Compared with the traditional ORB algorithm and other methods in the experiment of image feature point extraction and matching，it was shown that the proposed algorithm had better efficiency.The trajectory comparison experiment with ORB_SLAM-modified algorithm and the analysis of the point cloud map showed that the proposed algorithm had higher reliability and accuracy.

Key words:improved ORB feature;progressive sample consensus;feature point matching;V-SLAM;3D point cloud composition

 一种基于改进ORB和PROSAC特征点匹配的V-SLAM算法_王红星.pdf