Vision-based sensors have shown significant performance, accuracy, and efficiency gain in Simultaneous Localization and Mapping (SLAM) systems in recent years. In this regard, Visual Simultaneous Localization and Mapping (VSLAM) methods refer to the SLAM approaches that employ cameras for pose estimation and map generation. We can see many research works that demonstrated VSLAMs can outperform traditional methods, which rely only on a particular sensor, such as a Lidar, even with lower costs. VSLAM approaches utilize different camera types (e.g., monocular, stereo, and RGB-D), have been tested on various datasets (e.g., KITTI, TUM RGB-D, and EuRoC) and in dissimilar environments (e.g., indoors and outdoors), and employ multiple algorithms and methodologies to have a better understanding of the environment. The mentioned variations have made this topic popular for researchers and resulted in a wide range of VSLAMs methodologies. In this regard, the primary intent of this survey is to present the recent advances in VSLAM systems, along with discussing the existing challenges and trends. We have given an in-depth literature survey of forty-five impactful papers published in the domain of VSLAMs. We have classified these manuscripts by different characteristics, including the novelty domain, objectives, employed algorithms, and semantic level. We also discuss the current trends and future directions that may help researchers investigate them.
Towards Next-Generation SLAM: A Survey on 3DGS-SLAM Focusing on Performance, Robustness, and Future Directions
Beijing Institute of Technology（北京理工大学）；University of Science and Technology Beijing（北京科技大学）；Nanyang Technological University（南洋理工大学）；Shanghai AI Laboratory（上海人工智能实验室）
MonoEM-GS: Monocular Expectation-Maximization Gaussian Splatting SLAM
Autonomous Intelligent Systems group, Computer Science Institute VI – Intelligent Systems and Robotics – and the Center for Robotics and the Lamarr Institute for Machine Learning and Artificial Intelligence, University of Bonn（波恩大学自主智能系统组、计算机科学研究所VI——智能系统与机器人学——以及机器人中心与拉马尔机器学习和人工智能研究所）
POV-SLAM: Probabilistic Object-Aware Variational SLAM in Semi-Static Environments
The University of Toronto Institute for Aerospace Studies（多伦多大学航空航天研究所）；The Technical University of Munich（慕尼黑工业大学）；Clearpath Robotics（Clearpath机器人公司）；The Technical University of Nuremberg（纽伦堡工业大学）
Mapping-Guided Task Discovery and Allocation for Robotic Inspection of Underwater Structures
Department of Mechanical Engineering, University of Washington（机械工程系，华盛顿大学）；Department of Industrial & Systems Engineering and Department of Mechanical Engineering, University of Washington（工业与系统工程系和机械工程系，华盛顿大学）
A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontiers
Instituto de Investigación en Ingeniería de Aragón (I3A)（阿拉贡工程研究院（I3A））；Universidad de Zaragoza（萨拉戈萨大学）；Massachusetts Institute of Technology（麻省理工学院）；Genius Sports（Genius体育）；UC San Diego（加州大学圣地亚哥分校）；Technion – Israel Institute of Technology（以色列理工学院）
SUPER - Sensitivity-based Uncertainty-aware Performance and Risk Assessment in Visual Inertial Odometry
Hertie Institute for Clinical Brain Research & Center for Integrative Neuroscience, University of Tübingen（图宾根大学赫尔蒂临床脑研究所在职机构及整合神经科学中心）；Intelligent Process Automation and Robotics Lab (IPR), Karlsruhe Institute of Technology (KIT), Germany, Artificial Intelligence Institute (AIIS), Seoul National University, Republic of Korea（卡尔斯鲁厄理工学院（KIT）智能过程自动化与机器人实验室（IPR）、人工智能研究所（AIIS）、首尔国立大学）
