Improved Stereo Vision Algorithms For Robot Navigation

The main motivation of this research is to find the best depth and direction for navigating a robot using stereo vision by solving the difficulties in finding disparity value for low information, noisy and tilted images as problem statement. An adaptive window method is implemented in three approach...

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Main Author:	Ranjbaran, Ali
Format:	Thesis
Language:	English
Published:	2013
Subjects:	TK1-9971 Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.usm.my/45047/1/Ali%20Ranjbaran24.pdf
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id	my-usm-ep.45047
record_format	uketd_dc
spelling	my-usm-ep.450472019-07-24T07:05:05Z Improved Stereo Vision Algorithms For Robot Navigation 2013-07 Ranjbaran, Ali TK1-9971 Electrical engineering. Electronics. Nuclear engineering The main motivation of this research is to find the best depth and direction for navigating a robot using stereo vision by solving the difficulties in finding disparity value for low information, noisy and tilted images as problem statement. An adaptive window method is implemented in three approaches. Approach 1 and 2 use common cost functions such as SSD, SAD and GB (gradient). Approach 3 uses a Linear-based function. By using adaptive method, the error in computing the disparity value for SSD is 12%, for Gradient-based is 10% and for Linear-based is 7%. SSD is 8 and 2 times faster than Gradient-based and Linear-based functions respectively. The linear-based technique with 50% more accurate than SSD is a suitable tool for stereo vision applications. The proposed denoising method for Lena and Barbara images are compared with ROF model by computing Total Variation (TV). When TV is 0.88 for noisy Lena image, ROF reduces TV to 0.21, Linear-based and Gradient-based decreases TV to 0.24 and 0.26 respectively. When TV is 0.88 for noisy Barbara image, TV is decreased to 0.68 by ROF, 0.62 by Linear-based and 0.65 by Gradient-based techniques. A stable platform system is designed and developed for stabilizing vision line horizontally for a moving robot. The system is equipped by a closed loop tilt and pan stabilizer using a dual-axis accelerometer sensor. It stabilizes the tilt and pan angles with 0.5 second time constant and steady state error lower that 0.025 radian. By using adaptive stereo matching that uses linear cost function, the quality of the best direction for navigation is improved to 88% of success rate. 2013-07 Thesis http://eprints.usm.my/45047/ http://eprints.usm.my/45047/1/Ali%20Ranjbaran24.pdf application/pdf en public phd doctoral Universiti Sains Malaysia Pusat Pengajian Kejuruteraan Elektrik & Elektronik
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	TK1-9971 Electrical engineering Electronics Nuclear engineering
spellingShingle	TK1-9971 Electrical engineering Electronics Nuclear engineering Ranjbaran, Ali Improved Stereo Vision Algorithms For Robot Navigation
description	The main motivation of this research is to find the best depth and direction for navigating a robot using stereo vision by solving the difficulties in finding disparity value for low information, noisy and tilted images as problem statement. An adaptive window method is implemented in three approaches. Approach 1 and 2 use common cost functions such as SSD, SAD and GB (gradient). Approach 3 uses a Linear-based function. By using adaptive method, the error in computing the disparity value for SSD is 12%, for Gradient-based is 10% and for Linear-based is 7%. SSD is 8 and 2 times faster than Gradient-based and Linear-based functions respectively. The linear-based technique with 50% more accurate than SSD is a suitable tool for stereo vision applications. The proposed denoising method for Lena and Barbara images are compared with ROF model by computing Total Variation (TV). When TV is 0.88 for noisy Lena image, ROF reduces TV to 0.21, Linear-based and Gradient-based decreases TV to 0.24 and 0.26 respectively. When TV is 0.88 for noisy Barbara image, TV is decreased to 0.68 by ROF, 0.62 by Linear-based and 0.65 by Gradient-based techniques. A stable platform system is designed and developed for stabilizing vision line horizontally for a moving robot. The system is equipped by a closed loop tilt and pan stabilizer using a dual-axis accelerometer sensor. It stabilizes the tilt and pan angles with 0.5 second time constant and steady state error lower that 0.025 radian. By using adaptive stereo matching that uses linear cost function, the quality of the best direction for navigation is improved to 88% of success rate.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Ranjbaran, Ali
author_facet	Ranjbaran, Ali
author_sort	Ranjbaran, Ali
title	Improved Stereo Vision Algorithms For Robot Navigation
title_short	Improved Stereo Vision Algorithms For Robot Navigation
title_full	Improved Stereo Vision Algorithms For Robot Navigation
title_fullStr	Improved Stereo Vision Algorithms For Robot Navigation
title_full_unstemmed	Improved Stereo Vision Algorithms For Robot Navigation
title_sort	improved stereo vision algorithms for robot navigation
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Kejuruteraan Elektrik & Elektronik
publishDate	2013
url	http://eprints.usm.my/45047/1/Ali%20Ranjbaran24.pdf
_version_	1747821445084348416

Improved Stereo Vision Algorithms For Robot Navigation

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