Traditional methods based on points cannot balance the detection speed and accuracy in LiDAR semantic segmentation. To address this issue, this paper proposes a multimodal fusion LiDAR semantic segmentation network. Semantic features are extracted through the point-grid module, spatial and contextual information is aggregated through the attention mechanism module, semantic segmentation is achieved through the 2D Fully Convolutional Network (FCN) feature fusion pyramid, and finally, information loss is reduced through the fusion of 2D and 3D features, and the weights are updated to optimize the model using the loss function. Verification of SemanticKITTI dataset indicates that this model achieves an average crossover ratio of 63.3%, and takes into account of real-time property and accuracy as compared with other algorithms, which significantly improves the accuracy of LiDAR semantic segmentation.

Automatic driving  /  LiDAR  /  Semantic segmentation  /  Deep learning