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Lightweight LiDAR, characterized by its ease of use and cost-effectiveness, offers advantages in road intersection information acquisition. This study used lightweight LiDAR to collect 3D point cloud data from an urban road intersection and propose a semantic segmentation model based on the improved RandLA-Net. Initially, raw data from multiple positions and perspectives were obtained, and complete road intersection point clouds were stitched together using the iterative closest point algorithm for sequential registration. Subsequently, a semantic segmentation method for point clouds based on the improved RandLA-Net was proposed. This method included a spatial information encoding module based on feature similarities and a feature enhancement module based on multi-pooling fusion. This model optimized the feature aggregation capabilities during downsampling with the weighted cross-entropy loss function applied to reduce the impact of input sample scale imbalances. In comparisons of the improved RandLA-Net with PointNet++ and RandLA-Net on the same dataset, our method showed improved segmentation accuracy for various categories. The overall prediction accuracy on two road intersection point cloud test sets was 87.68% and 89.61%, with average F1 scores of 82.76% and 80.61%, respectively. Most notably, the prediction accuracy for road surface areas reached 94.48% and 94.79%. The results show that our model can enrich the spatial feature expression of input data and enhance semantic segmentation performance in road intersection scenarios.
Ren et al. (Sun,) studied this question.