Automobile indexation from 3D point clouds of urban scenarios

• Main Authors: Ramirez-Pedraza Alfonso, González-Barbosa José-Joel, Ramirez-Pedraza Raymundo, González-Barbosa Erick-Alejandro, Hurtado-Ramos Juan-Bautista
• Format: Article
• Language: English
• Published: Taylor & Francis Group 2021-07-01
• Series: Automatika
• Subjects: automobile indexation; 3d points cloud; segmentation; indexing
• Online Access: http://dx.doi.org/10.1080/00051144.2021.1947609

In this paper, we introduce a methodology for the detection and segmentation of automobiles in urban scenarios. We use the LiDAR Velodyne HDL-64E to scan the surroundings. The method is comprised of three steps: (1) remove facades, ground plan, and unstructured objects, (2) smoothing data using robust principal component analysis (RPCA), and finally, (3) unstructured objects model and indexing. The dataset is partitioned into training with 4500 objects and test with 3000 objects. Mean Shift thresholds, the filter, the Delaunay parameters, and the histogram modelling are optimized via ROC analysis. It is observed that the car scan quality affects our method to a lesser degree when compared with state-of-the-art methods.