The invention discloses a visual SLAM method based on point-line fusion, and the method comprises the steps: firstly inputting an image, predicting the
pose of a camera, extracting a feature point ofthe image, and estimating and extracting a feature line through the
time sequence information among a plurality of visual angles; and matching the feature points and the feature lines, tracking the features in front and back frames, establishing inter-frame association, optimizing the
pose of the current frame, and optimizing the two-dimensional feature lines to improve the integrity of the feature lines; judging whether the current
key frame is a
key frame or not, if yes, adding the
key frame into the map, updating three-dimensional points and lines in the map, performing joint optimization on the current key frame and the adjacent key frame, and optimizing the
pose and three-dimensional characteristics of the camera;and removing a part of external points and redundant key frames; and finally, performing
loopback detection on the key frame, if the current key frame and the previous frame are similar scenes, closing
loopback, and performing
global optimization once to eliminate accumulated errors. Under an SLAM
system framework based on points and lines, the line extraction speed and the feature line integrity are improved by utilizing the sequential relationship of
multiple view angle images, so that the pose precision and the map reconstruction effect are improved.