Work

Evaluación de técnicas de agrupamiento para segmentación de nubes de puntos LIDAR

Year: 2023

Type: article

Abstract: LIDAR point clouds are helpful for industrial robots, autonomous vehicles, and robotic urban search and rescue because they provide precise distance information. These data sets show indoor and outdoor scenarios with automobiles, people, buildings, and other objects. The information is presented densely and unstructured in 3-dimensional Cartesian coordinates. One of the first tasks in the analysis of the environment shown is the segmentation of the situations. This study evaluates various unsupervised clustering techniques for segmenting LIDAR point cloud data sets. An examination of the classes visually and the computation of clustering metrics serve to examine the behavior of the K-means, DBSCAN, BIRCH, and Mean Shift algorithms in various indoor and outdoor scenarios.

Source: JAIIO, Jornadas Argentinas de Informática

Authors: Urbano Pintos, Nicolás, Lacomi, Héctor Alberto, Lavorato, Mario Blas

Institution: National Technological University

Language: Spanish

Topic: Advanced Clustering Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: diamond

OpenAlex ID: W7215545955

FWCI:

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Related to: 10