Terrain classification for mobile robot navigation
92 p.
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2010
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sg-ntu-dr.10356-391752023-07-04T16:31:13Z Terrain classification for mobile robot navigation Karthikeyan Asaithambi. Wijerupage Sardha Wijesoma School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Robotics 92 p. Terrain classification is an important problem that still remains to be solved for autonomous robot vehicle guidance. Often, obstacle detection systems are used which cannot distinguish between solid obstacles such as rocks or soft obstacles such as tall patches of grass. It can also be used to recognize sand roads or other drivable areas. The aim of this project is to implement a terrain classification algorithm. The algorithm comprises of two main modules - color thresholding module and a neural network module. The initial input terrain image with distinct features namely sky, road, tree blob is given as an input to the color thresholding module. The input stream of images is first preprocessed by resizing the images for standardizing the images to a standard image space. Master of Science (Computer Control and Automation) 2010-05-21T04:47:19Z 2010-05-21T04:47:19Z 2007 2007 Thesis http://hdl.handle.net/10356/39175 application/pdf |
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DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Robotics Karthikeyan Asaithambi. Terrain classification for mobile robot navigation |
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92 p. |
author2 |
Wijerupage Sardha Wijesoma |
author_facet |
Wijerupage Sardha Wijesoma Karthikeyan Asaithambi. |
format |
Theses and Dissertations |
author |
Karthikeyan Asaithambi. |
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Karthikeyan Asaithambi. |
title |
Terrain classification for mobile robot navigation |
title_short |
Terrain classification for mobile robot navigation |
title_full |
Terrain classification for mobile robot navigation |
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Terrain classification for mobile robot navigation |
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Terrain classification for mobile robot navigation |
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terrain classification for mobile robot navigation |
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2010 |
url |
http://hdl.handle.net/10356/39175 |
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1772826341471682560 |