Visual‐SLAM Classical Framework and Key Techniques: A Review

Abstract

With the significant increase in demand for artificial intelligence, environmental map reconstruction has become a research hotspot for obstacle avoidance navigation, unmanned operations, and virtual reality. The quality of the map plays a vital role in positioning, path planning, and obstacle avoidance. This review starts with the development of SLAM (Simultaneous Localization and Mapping) and proceeds to a review of V‐SLAM (Visual‐SLAM) from its proposal to the present, with a summary of its historical milestones. In this context, the five parts of the classic V‐SLAM framework— visual sensor, visual odometer, backend optimization, loop detection, and mapping—are explained separately. Meanwhile, the details of the latest methods are shown; VI‐SLAM (Visual inertial SLAM) is reviewed and extended. The four critical techniques of V‐SLAM and its technical difficulties are summarized as feature detection and matching, selection of keyframes, uncertainty technology, and expression of maps. Finally, the development direction and needs of the V‐SLAM field are proposed.