Original scientific paper
https://doi.org/10.1080/00051144.2021.1947609
Automobile indexation from 3D point clouds of urban scenarios
Ramirez-Pedraza Alfonso
; Cátedra CONACYT – Centro de Investigaciones en Óptica A.C., León, Guanajuato, México
González-Barbosa José-Joel
; Instituto Politécnico Nacional, CICATA-Qro, Querétaro, México
Ramirez-Pedraza Raymundo
; CINVESTAV, Zapopan, Jalisco, México
González-Barbosa Erick-Alejandro
; Tecnológico Nacional de México/ITS de Irapuato, Irapuato, Guanajuato, México
Hurtado-Ramos Juan-Bautista
; Instituto Politécnico Nacional, CICATA-Qro, Querétaro, México
Abstract
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.
Keywords
Automobile indexation; 3D points cloud; segmentation; indexing
Hrčak ID:
269845
URI
Publication date:
20.10.2021.
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