Outdoor LiDAR point cloud classification algorithm based on multilevel point cluster feature fusion

Point cloud classification, as one of the key techniques for point cloud data processing, is an important step for the application of point cloud data. However, single-point-based point cloud classification faces the challenge of poor robustness, and single-scale point clusters only consider a single neighborhood, leading to insufficient feature representation. In addition, numerous cluster-based classification methods require further development in constructing point clusters and extracting their features for representing point cloud objects. To address these issues, we present a point cloud classification algorithm based on multi-level aggregated features. In our method, we employ a multi-level point cluster construction approach based on MLPCS (Multi-level Point Cluster Segmentation), which divides the original point cloud into three different levels of point clusters by voxel downsampling and rescanning, Voxel-Meanshift, and Voxel-DBSCAN (Density-Based Spatial Clustering of Applications with Noise). The features for each level of point clusters are extracted and their representation is improved by adopting methods like max pooling, Bag of Words (BoW) and K-Means. The multi-level point cluster features are then aggregated and combined with a random forest classifier to achieve automatic classification of point clouds. Finally, we conducted ablation and comparison experiments to verify the effectiveness and advantages of the algorithm. Our method achieved classification accuracy/Kappa coefficient of 99.88 %/99.86 % and 93.44 %/83.61 % respectively in the experiments on two sets of large-scale outdoor scene data, and the ablation experiments confirmed the effectiveness of our algorithm.