| Citation: | WANG P，HAO W L，NI C，et al. An overview of visual SLAM methods[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（2）：359-367 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0376
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Simultaneous localization and mapping (SLAM) enables mobile robots to calculate their position and pose by independently building an environment model during movement without any environmental prior conditions by carrying specific sensors. It can greatly improve the autonomous navigation ability of mobile robots and their adaptability to different application environments, and contribute to the subsequent implementation of dynamic path planning, real-time obstacle avoidance and multi-robot collaboration. Visual SLAM refers to using the camera as an external sensor to collect ambient information to create a map and estimate the robot’s own position in real-time. The study describes and examines the various feature detection approaches, back-end optimization, loop closure detection, and the application of visual SLAM in a dynamic environment in addition to introducing the standard classical visual SLAM methods and the visual SLAM methods mixed with deep learning. This study addresses the current state-of-the-art in research and the potential growth of visual SLAM in the future before summarizing the issues with visual SLAM raised here.
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