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

Authors

  • Paúl Tinizaray Escuela Politécnica Nacional, Ecuador https://orcid.org/0000-0003-2258-9701
  • Wilbert Aguilar Universidad de las Fuerzas Armadas - ESPE, Ecuador
  • José Lucio Escuela Politécnica Nacional, Ecuador

DOI:

https://doi.org/10.4114/intartif.vol25iss70pp50-63

Keywords:

Machine learning, floor detection, convolutional neural network, NYU-v2 dataset

Abstract

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.

Downloads

Download data is not yet available.

Downloads

Published

2022-11-23

How to Cite

Tinizaray, P., Aguilar, W., & Lucio, J. (2022). Fast segmentation of point clouds using a convolutional neural network for helping visually impaired people find the closest traversable region. Inteligencia Artificial, 25(70), 50–63. https://doi.org/10.4114/intartif.vol25iss70pp50-63