A Novel Obstacle Detection Method Based On Distortion Of Laser Pattern

Most visual based obstacle detection methods usually detect obstacles on the whole image, which are influenced by complex background and various illuminations. This paper proposes a novel and simple obstacle detection method DLP based on analyzing Distortion of Laser Pattern. To reduce computation, a laser pattern with two cross lines is used to project onto the front ground. Firstly, a segmentation algorithm based on color and edge is proposed to extract laser pixels. Then, skeletons of laser pattern are quickly extracted using table scanning strategy to further reduce noise. Secondly, seed-filling is used to extract the region of skeletons, and then laser lines are detected by Hough and merged by region based rule. Finally, according to the distortion of laser pattern projected on different obstacles, we propose a hierarchical discriminant model, which can classify the front scene as free or having obstacles. And the obstacle can be further classified as raised or sunken obstacle, and ascending or descending stairs. Experimental results show the robustness and effectiveness of our DLP method in different scenes, including indoor and outdoor, day and night with 10f/s.