Clustering LiDAR Data with K-means and DBSCAN

Abstract

Multi-object detection is an essential aspect of autonomous driving systems to guarantee the safety of selfdriving vehicles. In this paper, two clustering methods, DBSCAN and K-means, are used to segment LiDAR data and recognize the objects detected by the sensors. The Honda 3D LiDAR Dataset (H3D) and BOSCH data acquired within the THEIA project were the datasets used. The clustering methods were evaluated in several traffic scenarios, with different characteristics, extracted from both datasets. To validate the clustering results, five internal indexes were computed for each scenario tested. The available ground truth data for the H3D dataset also enabled the computation of 3 basic external indexes and a composite external index, which is newly proposed. A method to compute reference bounding boxes is presented using the available labels from H3D. The overall results indicate that K-means outperformed DBSCAN in the internal validation indexes Silhouette, C-index, and Calinski-Harabasz, and DBSCAN performed better than K-means in the Dunn and Davies-Bouldin indexes. The external validation indexes indicated that DBSCAN produces the best results, supporting the fact that density clustering is well-suited for LiDAR segmentation.