Open AccessAn improved feature matching ORB-SLAM algorithm
Author(s) -
Qiang Li,
Jia Kang,
Yangxi Wang,
Xiaofang Cao
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1693/1/012068
Subject(s) - orb (optics) , bundle adjustment , computer science , feature (linguistics) , algorithm , artificial intelligence , frame (networking) , simultaneous localization and mapping , blossom algorithm , matching (statistics) , graph , set (abstract data type) , feature matching , mobile robot , computer vision , robot , mathematics , image (mathematics) , philosophy , linguistics , statistics , telecommunications , theoretical computer science , programming language
To solve the problems of low accuracy and poor real-time performance in traditional mobile robot vision simultaneous positioning and map construction (SLAM), the original algorithm was improved. First, the ORB features of adjacent images are extracted, and the PROSAC algorithm is used to achieve feature point matching. At the same time, the PROSAC algorithm is improved and optimized, and the execution time of the optimized PROSAC algorithm is significantly reduced; finally, based on the graph optimization model, a global Bundle Adjustment algorithm based on the largest common-view weight frame is proposed to achieve dense and sparse map creation. The algorithm is verified by Tum data set, and the experiment shows that the root mean square error has dropped significantly. The results on the data set effectively prove the effectiveness of the algorithm in this paper.