This review paper provides a comprehensive overview of Visual SLAM (V-SLAM) in robotics, focusing on its evolution, properties, and future applications. V-SLAM plays a crucial role in robotic systems, particularly for interactive and collaborative mobile robots, by enabling simultaneous localization and mapping (SLAM). The paper highlights the growing complexity in real-world robotic tasks and the need for efficient V-SLAM methods. It chronologically presents the development of SLAM methods, emphasizing key principles and providing comparative analyses. The integration of the robotic ecosystem with a robot operating system (ROS) as middleware is discussed, along with essential V-SLAM benchmark datasets and illustrative workflows for each method. The paper is divided into six sections: 1. **Introduction**: Discusses the importance of robotics and SLAM, and the role of V-SLAM in robotic applications. 2. **Visual SLAM Paradigm**: Explains the fundamental concepts of V-SLAM, including data acquisition, system localization, map formation, and loop closure. 3. **State-of-the-Art V-SLAM Methods**: Reviews the latest advancements in V-SLAM, including only visual SLAM, visual-inertial SLAM, and RGB-D SLAM. 4. **Visual SLAM Evolution and Datasets**: Traces the evolution of V-SLAM methods and highlights significant benchmark datasets. 5. **Evaluation and Selection Criteria**: Provides guidelines for selecting appropriate V-SLAM methods based on evaluation criteria. 6. **Conclusion**: Summarizes the key points and contributions of the review. The paper emphasizes the importance of V-SLAM in robotics, offering a detailed analysis of various V-SLAM methods and their applications, making it a valuable resource for researchers and developers in the field.This review paper provides a comprehensive overview of Visual SLAM (V-SLAM) in robotics, focusing on its evolution, properties, and future applications. V-SLAM plays a crucial role in robotic systems, particularly for interactive and collaborative mobile robots, by enabling simultaneous localization and mapping (SLAM). The paper highlights the growing complexity in real-world robotic tasks and the need for efficient V-SLAM methods. It chronologically presents the development of SLAM methods, emphasizing key principles and providing comparative analyses. The integration of the robotic ecosystem with a robot operating system (ROS) as middleware is discussed, along with essential V-SLAM benchmark datasets and illustrative workflows for each method. The paper is divided into six sections: 1. **Introduction**: Discusses the importance of robotics and SLAM, and the role of V-SLAM in robotic applications. 2. **Visual SLAM Paradigm**: Explains the fundamental concepts of V-SLAM, including data acquisition, system localization, map formation, and loop closure. 3. **State-of-the-Art V-SLAM Methods**: Reviews the latest advancements in V-SLAM, including only visual SLAM, visual-inertial SLAM, and RGB-D SLAM. 4. **Visual SLAM Evolution and Datasets**: Traces the evolution of V-SLAM methods and highlights significant benchmark datasets. 5. **Evaluation and Selection Criteria**: Provides guidelines for selecting appropriate V-SLAM methods based on evaluation criteria. 6. **Conclusion**: Summarizes the key points and contributions of the review. The paper emphasizes the importance of V-SLAM in robotics, offering a detailed analysis of various V-SLAM methods and their applications, making it a valuable resource for researchers and developers in the field.