Detection of drivable areas on unstructured roads fused with point cloud space and reflection intensity
Drivable area detection aims to detect and extract areas where intelligent vehicles can travel on the road.The current mainstream detection method is mainly based on the spatial feature of three-dimensional light detection and ranging(3D-LIDAR),which is difficult to deal with unstructured roads without clear spatial features at the edge of the road surface.To this end,this paper proposes a drivable area detection method for unstructured roads based on the fusion of point cloud space and reflection intensity.First,the cylindrical coordinate system detection model based on the spatial features is im-proved by fusing reflection intensity factors;then,using intensity and dimensionality reduction space detection to optimize the ring detection model with low detection accuracy,and combining it with the cylindrical coordinate system detection model to improve the detection accuracy of the method;finally,a comparative experiment is carried out on the self-recorded actual road dataset.The experimental results show that the method in this paper significantly improves the success rate and accuracy of the drivable area detection on unstructured roads,and it also has good results on structured roads.
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