Visual simultaneous localization and mapping: a survey

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• 作者：Jorge Fuentes-Pacheco ; José Ruiz-Ascencio…
• 关键词：Visual SLAM ; Salient feature selection ; Image matching ; Data association ; Topological and metric maps
• 刊名：Artificial Intelligence Review
• 出版年：2015
• 出版时间：January 2015
• 年：2015
• 卷：43
• 期：1
• 页码：55-81
• 全文大小：368 KB
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• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Science, general
  Complexity
  
• 出版者：Springer Netherlands
• ISSN：1573-7462

文摘

Visual SLAM (simultaneous localization and mapping) refers to the problem of using images, as the only source of external information, in order to establish the position of a robot, a vehicle, or a moving camera in an environment, and at the same time, construct a representation of the explored zone. SLAM is an essential task for the autonomy of a robot. Nowadays, the problem of SLAM is considered solved when range sensors such as lasers or sonar are used to built 2D maps of small static environments. However SLAM for dynamic, complex and large scale environments, using vision as the sole external sensor, is an active area of research. The computer vision techniques employed in visual SLAM, such as detection, description and matching of salient features, image recognition and retrieval, among others, are still susceptible of improvement. The objective of this article is to provide new researchers in the field of visual SLAM a brief and comprehensible review of the state-of-the-art.