This paper presents a comprehensive review of visual simultaneous localization and mapping (V-SLAM) for robotics, covering its evolution, properties, and future applications. The authors analyze the latest V-SLAM methodologies, providing clear selection criteria for researchers and developers. The paper outlines the chronological development of SLAM methods, highlighting key principles and comparing different approaches. It discusses the integration of the robotic ecosystem with a robot operating system (ROS) as middleware, explores essential V-SLAM benchmark datasets, and presents demonstrative figures for each method's workflow. The review focuses on the integration of the robotic ecosystem with ROS as middleware, explores essential V-SLAM benchmark datasets, and presents demonstrative figures for each method's workflow. The paper discusses the evolution of V-SLAM, the most commonly used datasets, and techniques for evaluating SLAM methods. It also provides a conclusion summarizing the key points of the review. The paper highlights the importance of V-SLAM in robotics, particularly for interactive and collaborative mobile robots. It discusses the role of V-SLAM in enabling robots to navigate and interact in human environments, reducing human effort and enhancing productivity. The paper also explores the use of various types of cameras, including monocular, stereo, and RGB-D cameras, in V-SLAM applications. The review covers the state-of-the-art of visual SLAM methods, including only visual SLAM, visual-inertial SLAM, and RGB-D SLAM. It discusses the methodologies, efficiency, time requirements, and processing capacity of these methods, as well as whether they are designed to run on-board or off-board computer systems. The paper also provides a comparative table of essential parameters and features for each V-SLAM method. The paper explores the evolution of V-SLAM and the datasets used in the field. It discusses the TUM RGB-D dataset and the EuRoC MAV benchmark dataset, which are important resources in the development and evaluation of V-SLAM methods. The review concludes with a summary of the key points of the study, emphasizing the importance of V-SLAM in robotics and its potential future applications.This paper presents a comprehensive review of visual simultaneous localization and mapping (V-SLAM) for robotics, covering its evolution, properties, and future applications. The authors analyze the latest V-SLAM methodologies, providing clear selection criteria for researchers and developers. The paper outlines the chronological development of SLAM methods, highlighting key principles and comparing different approaches. It discusses the integration of the robotic ecosystem with a robot operating system (ROS) as middleware, explores essential V-SLAM benchmark datasets, and presents demonstrative figures for each method's workflow. The review focuses on the integration of the robotic ecosystem with ROS as middleware, explores essential V-SLAM benchmark datasets, and presents demonstrative figures for each method's workflow. The paper discusses the evolution of V-SLAM, the most commonly used datasets, and techniques for evaluating SLAM methods. It also provides a conclusion summarizing the key points of the review. The paper highlights the importance of V-SLAM in robotics, particularly for interactive and collaborative mobile robots. It discusses the role of V-SLAM in enabling robots to navigate and interact in human environments, reducing human effort and enhancing productivity. The paper also explores the use of various types of cameras, including monocular, stereo, and RGB-D cameras, in V-SLAM applications. The review covers the state-of-the-art of visual SLAM methods, including only visual SLAM, visual-inertial SLAM, and RGB-D SLAM. It discusses the methodologies, efficiency, time requirements, and processing capacity of these methods, as well as whether they are designed to run on-board or off-board computer systems. The paper also provides a comparative table of essential parameters and features for each V-SLAM method. The paper explores the evolution of V-SLAM and the datasets used in the field. It discusses the TUM RGB-D dataset and the EuRoC MAV benchmark dataset, which are important resources in the development and evaluation of V-SLAM methods. The review concludes with a summary of the key points of the study, emphasizing the importance of V-SLAM in robotics and its potential future applications.