Visual-Based Simultaneous Localization and Mapping (VSLAM) Techniques for Robots: A Scientific Review


  • Fayez Saeed Faizi Department of Electrical and Computer Engineering. University of Duhok, Iraq-Kurdistan, Iraq and (Department of Energy Engineering. Duhok Polytechnic University, Iraq-Kurdistan, Iraq)
  • Ahmed Khorsheed Alsulaifanie Department of Electrical and Computer Engineering. University of Duhok, Iraq-Kurdistan, Iraq



The main problem facing autonomous robots is to navigate in an environment with the ability to determine its location and simultaneously build a map, SLAM technique can formulate this requirement efficiently. In this paper Filter-based, Graph-based, and AI-based Visual-SLAM techniques have been reviewed. The review shows that the first method suffers from high computations when the number of landmarks increases. The Graph-based algorithms are exposed to drift-error problems which cause a delocalization and require optimization. The AI-based vSLAM has the advantage of not-having complicated mathematical models in the algorithm, and it shows an efficient performance in various environments. The reviewed algorithms utilize different cameras including mono, stereo, and RGB-D cameras. The low-cost RGB-D cameras encourage implementation in modern autonomous robots. This work introduces a scientific-based overview of vSLAM to the reader, by explaining all phases of SLAM, the state-of-the-art algorithms, highlighting the strengths and weaknesses of each paradigm. 


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How to Cite

Saeed Faizi, F., & Khorsheed Alsulaifanie, A. (2023). Visual-Based Simultaneous Localization and Mapping (VSLAM) Techniques for Robots: A Scientific Review. Academic Journal of Nawroz University, 12(3), 213–229.