Vision-based simultaneous localization and mapping (SLAM) is a research hotspot in the field of intelligent driving. However

for the scenes that contain moving targets or inconspicuous close-range features

it is easy to cause ineffective or inaccurate pose estimation between frames. To solve this problem

this paper proposes a SLAM algorithm based on road structured features and scene semantic information. First

for the problem of target moving

a semantic segmentation neural network with an improved pyramid pooling module was designed to obtain the target category corresponding to each pixel in the image. The segmentation results were taken as basis for the elimination of moving points

which avoids the problem of low accuracy of pose calculation caused by moving points participating in feature matching. Then

in view of the lack of effective feature points

the road area in the image was determined based on v-disparity algorithm

and disparity function was obtained to calculate the accurate disparity value of the pixels on the road. Furthermore

a pose calculation method based on road structured features (e.g.

lane lines

road boundaries

pavement traffic markings) was proposed. Finally

scene experiments were carried out and results show that the absolute trajectory error of the improved algorithm proposed in this paper was smaller than that of the original algorithm

which proves that the proposed method has higher pose estimation accuracy in scenes with moving targets or inconspicuous close-range features. In addition

an effective dense point cloud map containing semantic information was established

which has good environmental adaptability.