Fast segmentation of point clouds using a convolutional neural network for helping visually impaired people find the closest traversable region

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      <subfield code="a">Fast segmentation of point clouds using a convolutional neural network for helping visually impaired people find the closest traversable region</subfield>
      <subfield code="c">Paúl Tinizaray, Wilbert Aguilar, José Lucio</subfield>
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      <subfield code="a">In this paper, we introduce an approach for helping visually impaired people to find the closest-to-user traversable region. The aim of our work is to reduce the computational cost of this task. For this purpose, we develop a convolutional neural network that classifies patches to segment floor regions in a point cloud. Segmented regions are evaluated by their size and position in the point cloud to identify the closest-to-user traversable region. We evaluate our approach using the NYU-v2 dataset and find that by searching only in the lower section of the point cloud, it is possible to reduce the processing time while finding the closest floor regions. Our approach reports a better processing time than related works, making it suitable to quickly find the closest-to-user traversable region in point clouds.

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      <subfield code="a">La copia digital se distribuye bajo licencia "Attribution 4.0 International (CC BY NC 4.0)"</subfield>
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      <subfield code="g">05/12/2022 Volumen 25 Número 70 - diciembre 2022 , p. 50-63</subfield>
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      <subfield code="t">Revista Iberoamericana de Inteligencia Artificial</subfield>
      <subfield code="d"> : IBERAMIA, Sociedad Iberoamericana de Inteligencia Artificial , 2018-</subfield>
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