基于稀疏卷积神经网络的车载激光雷达点云语义分割方法

The semantic segmentation of point cloud obtained by on-board lidar scanning is one of the important means for ensuring driving safety and enhancing the driver's understanding of surrounding environment. Due to memory limitation and the sparse characteristics of large-scale point cloud scenes, directly continue to apply the traditional neural network approach to the large-scale point cloud scenario does not work well. Therefore, taking the advantage of the sparseness of large-scale point cloud, sparse convolutional neural network (sparse CNN) is used to extract the characteristics of voxel cloud in this paper. With consideration of the information loss cause by density inconsistence in point-wise processing sub-branch suppressed point cloud data, additional 3D-CA and 3D-SA modules are designed to improve the characteristics extraction of sparse CNN. The results of experiment show that compared with traditional convolutional neural network method and point cloud projection on plane method, the sparse CNN method for the semantic segmentation of large scale point cloud can have 4.1% and 3.4% higher IOU respectively, demonstrating the effectiveness of the method adopted.