Fast segmentation of point clouds using a convolutional neural network for helping visually impaired people find the closest traversable region
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 reg...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Asociación Española para la Inteligencia Artificial
2022-11-01
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| Series: | Inteligencia Artificial |
| Subjects: | |
| Online Access: | https://journal.iberamia.org/index.php/intartif/article/view/735 |
| _version_ | 1811303941384699904 |
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| author | Paúl Tinizaray Wilbert Aguilar José Lucio |
| author_facet | Paúl Tinizaray Wilbert Aguilar José Lucio |
| author_sort | Paúl Tinizaray |
| collection | DOAJ |
| description |
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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| first_indexed | 2024-04-13T07:56:59Z |
| format | Article |
| id | doaj.art-8e5253b1ce514d9ab8da9743ff837e77 |
| institution | Directory Open Access Journal |
| issn | 1137-3601 1988-3064 |
| language | English |
| last_indexed | 2024-04-13T07:56:59Z |
| publishDate | 2022-11-01 |
| publisher | Asociación Española para la Inteligencia Artificial |
| record_format | Article |
| series | Inteligencia Artificial |
| spelling | doaj.art-8e5253b1ce514d9ab8da9743ff837e772022-12-22T02:55:23ZengAsociación Española para la Inteligencia ArtificialInteligencia Artificial1137-36011988-30642022-11-01257010.4114/intartif.vol25iss70pp50-63Fast segmentation of point clouds using a convolutional neural network for helping visually impaired people find the closest traversable regionPaúl Tinizaray0Wilbert Aguilar1José Lucio2Escuela Politécnica Nacional, EcuadorUniversidad de las Fuerzas Armadas - ESPE, EcuadorEscuela Politécnica Nacional, Ecuador 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. https://journal.iberamia.org/index.php/intartif/article/view/735Machine learningfloor detectionconvolutional neural networkNYU-v2 dataset |
| spellingShingle | Paúl Tinizaray Wilbert Aguilar José Lucio Fast segmentation of point clouds using a convolutional neural network for helping visually impaired people find the closest traversable region Inteligencia Artificial Machine learning floor detection convolutional neural network NYU-v2 dataset |
| title | Fast segmentation of point clouds using a convolutional neural network for helping visually impaired people find the closest traversable region |
| title_full | Fast segmentation of point clouds using a convolutional neural network for helping visually impaired people find the closest traversable region |
| title_fullStr | Fast segmentation of point clouds using a convolutional neural network for helping visually impaired people find the closest traversable region |
| title_full_unstemmed | Fast segmentation of point clouds using a convolutional neural network for helping visually impaired people find the closest traversable region |
| title_short | Fast segmentation of point clouds using a convolutional neural network for helping visually impaired people find the closest traversable region |
| title_sort | fast segmentation of point clouds using a convolutional neural network for helping visually impaired people find the closest traversable region |
| topic | Machine learning floor detection convolutional neural network NYU-v2 dataset |
| url | https://journal.iberamia.org/index.php/intartif/article/view/735 |
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