ISBN
Formato digital
979-13-87837-54-9
Fecha de publicación
06-10-2025
Licencia
D. R. © Copyright 2025. Alma Y. Alanis, Jorge Galvez, Omar Avalos, Eduardo Méndez-Palos, Jorge D. Rios, Adriana Peña Perez-Negron & Gabriel Martínez Soltero
Todos los contenidos de esta obra se comparten bajo la licencia Creative Commons Atri-bución/Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0). Esto implica que no está autorizado el uso comercial de la obra original ni de las eventuales obras derivadas, las cuales deberán distribuirse bajo la misma licencia que rige la obra original. No obstante, se permite a terceros compartir el contenido siempre y cuando se reconozca debidamente la autoría y la publicación original en esta editorial.
Monserrat Carlos
Universidad de Guadalajara
0009-0004-0661-1758
Carlos Villaseñor
Universidad de Guadalajara
0000-0003-0802-0121
Javier Enrique Gómez Avila
Universidad de Guadalajara
0000-0002-9724-1729
Nancy Arana Daniel
Universidad de Guadalajara
0000-0002-8803-9502
Acerca de
In recent years, object detection has become a key topic in navigation, both for robotics and autonomous driving. Most object detection systems rely heavily on cameras; however, these present limitations, as they capture images in a 2D plane, which leads to a loss of information about depth and the spatial relationships between multiple objects. Point clouds provide a geometric 3D representation that is useful for different robotics operations like path planning or object avoidance.
The use of light detection and ranging (LiDAR) technology for object detection was not initially widespread, because of its capability but rather due to its high cost. However, as LiDAR has become more affordable, it has gained traction as a viable method for study and application, especially for more common equipment and robotic applications.
This paper is organized as fallow: First in Section 2, we introduce the Con- formal Net, used to estimate the probability of seeing each object. In Section 3, we present the Germinal Center Optimization (GCO) Algorithm. In Section 4, we present the proposed method for object detection. In Section 5, we present our results and comparison with other state-of-the-art algorithms. Finally in section 6 we offer a conclusion.
Referencias
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