2014
DOI: 10.5772/58900
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A Taxonomy of Vision Systems for Ground Mobile Robots

Abstract: This paper introduces a taxonomy of vision systems for ground mobile robots. In the last five years, a significant number of relevant papers have contributed to this subject. Firstly, a thorough review of the papers is proposed to discuss and classify both past and the most current approaches in the field. As a result, a global picture of the state of the art of the last five years is obtained. Moreover, the study of the articles is used to put forward a comprehensive taxonomy based on the most up-to-date rese… Show more

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Cited by 34 publications
(19 citation statements)
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“…This process is known as feature extraction and can be accomplished in different ways, as it is discussed in [28]. In this paper, we follow the scheme proposed in [29], where a combination of multiple low-level visual features is explored.…”
Section: Feature Extraction and Descriptor Generationmentioning
confidence: 99%
“…This process is known as feature extraction and can be accomplished in different ways, as it is discussed in [28]. In this paper, we follow the scheme proposed in [29], where a combination of multiple low-level visual features is explored.…”
Section: Feature Extraction and Descriptor Generationmentioning
confidence: 99%
“…Although the bioinspired vision solutions in mobile robot navigation mostly extract natural salient features, in many practical applications artificial landmarks are employed in order to simplify and speed-up the image processing and to make the detection and recognition of features more reliable [15]. Visual self-localization algorithms are susceptible to errors due to unpredictable changes in the environment [11], and require much computing power to process natural features, e.g.…”
Section: Related Workmentioning
confidence: 99%
“…Notice that a global descriptor based on textons has already been used in off-road mobile robots, see for example [3,25]. However, recent reports in the field of computer vision confirm that the Gist descriptor slightly outperforms textons [26].…”
Section: Gist Descriptormentioning
confidence: 99%