Novas abordagens para segmentação de nuvens de pontos aplicadas à robótica autônoma e reconstrução 3D
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2016-08-12
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Universidade Federal de Goiás
Resumo
Depth sensing methods yield point clouds that represent neighboring surfaces. Interpreting
and extracting information from point clouds is an established field, full of yet unsolved
challenges. Classic image processing algorithms are not applicable or must be adapted
because the organized structure of 2D images is not available. This work presents three
contribution to the field of point cloud processing and segmentation. These contributions
are the results of investigations carried out at the Laboratory for Education and Innovation
in Automation – LEIA, aiming to advance the knowledges related to applying spacial
sensing to autonomous robotics. The first contribution consists of a new algorithm, based
on evolutionary methods, for extracting planes from point clouds. Based on the method
proposed by Bazargani, Mateus e Loja (2015), this contribution consists of adopting
evolutionary strategies in place of genetic algorithms making the process less sensitive to
user-defined parameters. The second contribution is a method for segmenting ground and
obstacles from point clouds for autonomous navigation, that utilizes the proposed plane
extraction algorithm. The use of a quadtree for adaptive area segmentation allows for
classifying points with high accuracy efficiently and with a time performance compatible
with low cost embedded devices. The third contribution is a variant of the proposed
segmentation method that is more noise tolerant and robust by incorporating a neural
classifier. The use of a neural classifier in place of simple thresholding makes the process
less sensitive to point cloud noise and faults, making it specially interesting for processing
point clouds obtained from real time stereo reconstruction methods. A through sensitivity,
accuracy, and efficiency analysis is presented for each algorithm. The dihedral angle
metric (angle between the detected plane and the reference polygons that share at least
one point) proposed by Bazargani, Mateus e Loja (2015) is used to quantify the plane
detection method accuracy. The ratio between the correctly classified points and the total
number of points is utilized as an accuracy metric for the ground segmentation methods.
Additionally, computing costs and execution times are considered and compared to the
main state-of-the-art methods.
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MARCON S., G. A. Novas abordagens para segmentação de nuvens de pontos aplicadas à robótica autônoma e reconstrução 3D. 2016. 112 f. Dissertação (Mestrado em Engenharia Elétrica e da Computação) - Universidade Federal de Goiás, Goiânia, 2016.