Technology of Graphic & Image
|
App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 11
3443-3449

Improved feature matching and dense-mapping algorithm based on ORB-SLAM3

Liu Chang
Dang Shuwen
Chen Li
School of Air Transport, Shanghai University of Engineering Science, Shanghai 201600, China
DOI: 10.19734/j.issn.1001-3695.2023.03.0098

Abstract

In view of the problems that the extracted feature points of the traditional ORB algorithm tend to accumulate in the texture-rich area and the high false matching rate, which cannot meet the requirements of high-precision positioning, and that the ORB-SLAM3 system cannot build a dense map, this paper proposed an improved ORB-GMS feature matching method based on ORB-SLAM3, and added dense-mapping thread to realize the construction of dense maps. Firstly, the feature points extraction process adopted the quadtree strategy to divide the image frame into several meshes, and extracted the best feature points in each mesh. Then, it replaced the motion smoothing constraint with a statistic that rejected incorrect matches during feature matching, and used a comparison of the number of matching pairs and thresholds within the neighborhood of matching pairs to quickly filter correct matches. Finally, it completed the positional estimation and constructed a dense point cloud map by keyframes and corresponding poses. Testing by RGB-D dataset from TUM, the improved algorithm can extract uniform feature points, the number of matches increases by 64.5% than ORB-SLAM3, increases by 4.7% than the GMS algorithm, and the matching elapsed time decreases by 20.4% than ORB-SLAM3, and decreases by 94.6% than the GMS algorithm, which proves that the improved algorithm is superior in feature point extraction and matching. And compared to the ORB-SLAM3, the accuracy of the improved algorithm increases by 3.75%, thus, demonstrating its feasibility and effectiveness in improving the localization accuracy and building dense maps.

Foundation Support

国家自然科学基金资助项目(52175103)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.03.0098
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 11
Section: Technology of Graphic & Image
Pages: 3443-3449
Serial Number: 1001-3695(2023)11-037-3443-07

Publish History

[2023-05-19] Accepted Paper
[2023-11-05] Printed Article

Cite This Article

刘畅, 党淑雯, 陈丽. 基于ORB-SLAM3的改进型特征匹配与稠密建图算法 [J]. 计算机应用研究, 2023, 40 (11): 3443-3449. (Liu Chang, Dang Shuwen, Chen Li. Improved feature matching and dense-mapping algorithm based on ORB-SLAM3 [J]. Application Research of Computers, 2023, 40 (11): 3443-3449. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)