基于多通道融合和极线约束的道路检测与定位

The use of computer binocular vision to study road detection and location plays an important role in realizing autonomous navigation of unmanned motion platforms. According to the binocular camera system model, a lane-line detection method based on multi-channel threshold fusion was proposed. The lane threshold and color information were combined to perform image threshold segmentation. The perspective transform and adaptive dynamic sliding window method were used to extract lane line pixels. The least squares method was adopted to fit road model, position according to the polar constraint relationship and project the result into the SLAM map. The experimental results show that the algorithm can accurately detect the lane line in the scenes of illumination change and shadow occlusion. Projecting the lane line information to the three-dimensional map can effectively fuse the lane information with the map information and improve the road perception ability.