2026-09-19T05:36:39Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/382712022-01-13T07:54:36Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Karl Iagnemma.
Halatci, Ibrahim
Massachusetts Institute of Technology. Dept. of Mechanical Engineering.
Massachusetts Institute of Technology. Department of Mechanical Engineering
2007-08-03T18:24:18Z
2007-08-03T18:24:18Z
2006
2006
http://hdl.handle.net/1721.1/38271
151219636
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2006.
Includes bibliographical references (leaves 63-66).
Autonomous rover operation plays a key role in planetary exploration missions. Rover systems require more and more autonomous capabilities to improve efficiency and robustness. Rover mobility is one of the critical components that can directly affect mission success. Knowledge of the physical properties of the terrain surrounding a planetary exploration rover can be used to allow a rover system to fully exploit its mobility capabilities. Here a study of multi-sensor terrain classification for planetary rovers in Mars and Mars-like environments is presented. Supervised classification algorithms for color, texture, and range features are presented based on mixture of Gaussians modeling. Two techniques for merging the results of these "low level" classifiers are presented that rely on Bayesian fusion and meta-classifier fusion. The performances of these algorithms are studied using images from NASA's Mars Exploration Rover mission and through experiments on a four-wheeled test-bed rover operating in Mars-analog terrain. It is shown that accurate terrain classification can be achieved via classifier fusion from visual features.
by Ibrahim Halatci.
S.M.
91 leaves
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Mechanical Engineering.
Vision-based terrain classification and classifier fusion for planetary exploration rovers
Thesis
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<Abstract>Autonomous rover operation plays a key role in planetary exploration missions. Rover systems require more and more autonomous capabilities to improve efficiency and robustness. Rover mobility is one of the critical components that can directly affect mission success. Knowledge of the physical properties of the terrain surrounding a planetary exploration rover can be used to allow a rover system to fully exploit its mobility capabilities. Here a study of multi-sensor terrain classification for planetary rovers in Mars and Mars-like environments is presented. Supervised classification algorithms for color, texture, and range features are presented based on mixture of Gaussians modeling. Two techniques for merging the results of these "low level" classifiers are presented that rely on Bayesian fusion and meta-classifier fusion. The performances of these algorithms are studied using images from NASA's Mars Exploration Rover mission and through experiments on a four-wheeled test-bed rover operating in Mars-analog terrain. It is shown that accurate terrain classification can be achieved via classifier fusion from visual features.</Abstract>
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