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A review of visual SLAM for robotics: evolution, properties, and future applications
Basheer Al-Tawil1, Thorsten Hempel1, Ahmed Abdelrahman1
1Institute for Information Technology and Communications, Otto-von-Guericke-University, Magdeburg, Germany.
Frontiers in Robotics and AI
|April 30, 2024
Summary
This study reviews visual simultaneous localization and mapping (V-SLAM) methods for robots. It provides selection criteria and analyzes V-SLAM evolution for better robotic applications.
Area of Science:
- Robotics
- Computer Vision
- Artificial Intelligence
Background:
- Visual simultaneous localization and mapping (V-SLAM) is vital for autonomous robots.
- Increasing robot complexity necessitates streamlined V-SLAM solutions.
- V-SLAM methods are essential for interactive and collaborative mobile robots.
Purpose of the Study:
- To present the latest V-SLAM methodologies.
- To offer selection criteria for V-SLAM approaches in robotic applications.
- To provide a chronological overview and comparative analysis of V-SLAM evolution.
Main Methods:
- Chronological presentation of SLAM method evolution.
- Comparative analysis of different V-SLAM techniques.
- Focus on Robot Operating System (ROS) integration and benchmark datasets.
Main Results:
- Detailed overview of V-SLAM methodologies.
- Key principles and comparative analyses highlighted.
- Demonstrative figures illustrating V-SLAM workflows.
Conclusions:
- Researchers and developers can select optimal V-SLAM methods.
- Enhanced understanding of V-SLAM's role in robotic systems.
- Facilitation of V-SLAM integration in the robotic ecosystem.
