Abstract
SLAM (hereinafter referred to as SLAM) refers to the autonomous mobile carrier in the unknown environment, through the data information obtained by its own sensor to achieve its own positioning, in addition to the technology can also continuously build and update the map in the process of carrier movement. Visual SLAM is a technology that uses visual sensors as input and uses dense perception of the surrounding environment to achieve SLAM function. Compared with the traditional SLAM method, visual SLAM can retain the semantic information in the environment while ensuring the accuracy, so as to expand the function of the carrier. This paper first introduces the milestone methods in the field of visual SLAM in chronological order, then introduces the standard flow of visual SLAM, and finally introduces the advantages and several typical excellent algorithms of visual SLAM that integrates point-and-line features.
