Abstract
The intersections are junctions of roads. Recognizing intersections based on LiDAR accurately and quickly is a key task for UGVs. In this paper, we propose an end-to-end real-time intersections recognition network (IRNet) with graph attention convolution based on graph classification and 3D LiDAR point cloud. Multiple evaluations on KITTI and Tunnel dataset demonstrate that our model performs better than other competitive models and meets the real-time recognition. We research the performance of our model under different number of input points, and certify that neither of two spatial transformation networks is effective for our model. Ablation experiments certify effectiveness of the proposed features skip connection.
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