A Biologically Inspired Approach for Robot Depth Estimation

Please use this identifier to cite or link to this item: http://hdl.handle.net/10045/78330
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Title: A Biologically Inspired Approach for Robot Depth Estimation
Authors: Martinez-Martin, Ester | Pobil, Angel P. del
Research Group/s: Robótica y Visión Tridimensional (RoViT)
Center, Department or Service: Universidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificial
Keywords: Biologically Inspired Approach | Robot Depth Estimation
Knowledge Area: Ciencia de la Computación e Inteligencia Artificial
Issue Date: 23-Aug-2018
Publisher: Hindawi Publishing Corporation
Citation: Computational Intelligence and Neuroscience. Volume 2018, Article ID 9179462, 16 pages. doi:10.1155/2018/9179462
Abstract: Aimed at building autonomous service robots, reasoning, perception, and action should be properly integrated. In this paper, the depth cue has been analysed as an early stage given its importance for robotic tasks. So, from neuroscience findings, a hierarchical four-level dorsal architecture has been designed and implemented. Mainly, from a stereo image pair, a set of complex Gabor filters is applied for estimating an egocentric quantitative disparity map. This map leads to a quantitative depth scene representation that provides the raw input for a qualitative approach. So, the reasoning method infers the data required to make the right decision at any time. As it will be shown, the experimental results highlight the robust performance of the biologically inspired approach presented in this paper.
Sponsor: This paper describes research done at UJI Robotic Intelligence Laboratory. Support for this laboratory was provided in part by Ministerio de Economía y Competitividad (DPI2015-69041-R) and by Universitat Jaume I.
URI: http://hdl.handle.net/10045/78330
ISSN: 1687-5265 (Print) | 1687-5273 (Online)
DOI: 10.1155/2018/9179462
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2018 Ester Martinez-Martin and Angel P. del Pobil. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Peer Review: si
Publisher version: https://doi.org/10.1155/2018/9179462
Appears in Collections:INV - RoViT - Artículos de Revistas

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