NELD-EC: Neighborhood-Effective-Line-Density-Based Euclidean Clustering for Point Cloud Segmentation.

For the problem that it is difficult to effectively cluster lidar point clouds with irregular shapes and uneven densities, a Neighborhood Effective Line Density (NELD)-based Euclidean Clustering (NELD-EC) algorithm is proposed in this paper. The NELD-EC algorithm first eliminates the interfering points within the neighborhood of the data point by utilizing the distance relationship and calculates the NELD of the data point using the effective neighborhood set without interfering points of the data point. The NELD of a data point is taken as the local density of that data point. Then, the NELD-EC algorithm conducts clustering processing using the NELD of all data points and uses the reciprocal of the harmonic average of the local densities of all data points within each cluster after clustering as the distance threshold for the data points within the cluster. Finally, the NELD-EC algorithm completes the clustering of the point cloud based on the adjusted adaptive distance threshold. The clustering experimental results on simulated point clouds, fixed point clouds, and sequential point clouds indicate that, compared with several other typical Euclidean clustering algorithms, the NELD-EC algorithm requires simpler parameters to be set, is less sensitive to the initial distance threshold, can effectively reduce the occurrence probabilities of over-segmentation and under-segmentation, and has strong stability in clustering performance. The NELD-EC algorithm is more suitable for processing sequential point clouds in actual dynamic and complex scenarios.