Research on V-SLAM Methods

Abstract

With the development of intelligent mobile robots, SLAM, especially V-SLAM, as the basic technology of robot localization and navigation, has the advantages of strong adaptability, high precision and strong intelligence compared with the traditional localization technology. It is widely used in smart devices such as unmanned aerial vehicle, automatic driving and sweeping robots. According to different implementation methods, the visual SLAM is divided into: filter V-SLAM based on probability model, key frame BA-based V-SLAM using nonlinear optimization theory, direct tracking of V-SLAM under the assumption of luminosity invariance, space occupying V-SLAM that focuses on building three-dimensional dense maps. This paper focuses on representative systems of various V-SLAMs and gives their respective applicable scenarios and characteristics. Finally, this article forecasts the development of V-SLAM combining with multi-information fusion technology, semantic deep and learning technology.