Vol. 5 No. 1 (2025): Issue 5
Articles

Simultaneous Localization and Mapping through Loop Closure Detection

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  • Andreas Kouroupos,
  • Haibo Wang
Andreas Kouroupos
Institute of Intelligent Systems and Robotics, Hellenic Mediterranean University
Haibo Wang
Heinz College of Information Systems and Public Policy, Carnegie Mellon University, Pittsburgh, 15213, USA

Published 2025-03-10

Keywords

  • Localization,
  • Mapping,
  • Loop Closure Detection,
  • Deep Learning,
  • Autonomous Navigation

How to Cite

Kouroupos, A., & Wang, H. (2025). Simultaneous Localization and Mapping through Loop Closure Detection. Generative Artificial Intelligence Research, 5(1). Retrieved from https://ojs.mri-pub.com/index.php/GAIR/article/view/103

Abstract

Simultaneous Localization and Mapping (SLAM) through loop closure detection is a crucial and challenging task in the field of robotics and autonomous navigation. Accurate and efficient SLAM systems are essential for various applications, such as self-driving vehicles and unmanned aerial vehicles. However, the current research faces difficulties in achieving robust loop closure detection and maintaining real-time performance. This paper addresses these challenges by proposing a novel approach that combines feature-based methods with deep learning techniques for loop closure detection. We conduct extensive experiments to demonstrate the effectiveness and efficiency of our method in improving SLAM accuracy and reducing computational costs. Our research contributes to advancing the capabilities of SLAM systems and paves the way for more reliable and intelligent autonomous systems.

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