A Comparative Study of 3D Plant Modeling Systems Based on Low-Cost 2D LiDAR and Kinect

Harold Murcia, David Sanabria, Dehyro Méndez, Manuel G. Forero

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

Morphological information of plants is an essential resource for different agricultural machine vision applications, which can be obtained from 3D models through reconstruction algorithms. Three dimensional modeling of a plant is an XYZ spatial representation used to determine its physical parameters from, for example, a point cloud. Currently two low-cost methods have gained popularity in terms of 3D object reconstructions in 360 employing rotating platforms, based on 2D LiDAR and Kinect. In this paper, these two techniques are compared by getting a 3D model of a Dracaena braunii specie and evaluating their performance. The results are shown in terms of their accuracy and time consumption using a Kinect V1 and a LiDAR URG-04LX-UG01, a well-performance low-cost scanning rangefinder from Hokuyo manufacturer. In terms of error calculation, the Kinect-based system probed to be more accurate than the LiDAR-based, with an error less than 20% in all plant measurements. In addition, the point cloud density reached with Kinect was approximately four times higher than with LiDAR. But, acquisition and processing time was about twice than LiDAR system.

Idioma originalInglés
Título de la publicación alojadaPattern Recognition - 13th Mexican Conference, MCPR 2021, Proceedings
EditoresEdgar Roman-Rangel, Ángel Fernando Kuri-Morales, José Francisco Martínez-Trinidad, Jesús Ariel Carrasco-Ochoa, José Arturo Olvera-López
EditorialSpringer Science and Business Media Deutschland GmbH
Páginas272-281
Número de páginas10
ISBN (versión impresa)9783030770037
DOI
EstadoPublicada - 2021
Evento13th Mexican Conference on Pattern Recognition, MCPR 2021 - Virtual, Online
Duración: 23 jun 202126 jun 2021

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen12725 LNCS
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia13th Mexican Conference on Pattern Recognition, MCPR 2021
CiudadVirtual, Online
Período23/06/2126/06/21

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