A New Real-Time Visual SLAM Algorithm Based on the Improved FAST Features

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• 作者：Liang Wang (17)
  Rong Liu (18)
  Chao Liang (17)
  Fuqing Duan (19)
  
• 刊名：Lecture Notes in Computer Science
• 出版年：2013
• 出版时间：2013
• 年：2013
• 卷：8008
• 期：1
• 页码：216-226
• 全文大小：1638KB
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• 作者单位：Liang Wang (17)
  Rong Liu (18)
  Chao Liang (17)
  Fuqing Duan (19)
  
  17. College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing, 100124, China
  18. Base Department, Beijing Institute of Fashion Technology, Beijing, 100029, China
  19. College of Information Science and Technology, Beijing Normal University, Beijing, 100875, China
  

文摘

The visual SLAM is less dependent on hardware, so it attracts growing interests. However, the visual SLAM, especially the Extend Kalman Filter-based monocular SLAM is computational expensive, and is hard to fulfill real-time process. In this paper, we propose an algorithm, which uses the binary robust independent elementary Features descriptor to describe the features from accelerated segment test feature aiming at improving feature points extraction and matching, and combines with the 1-point random sample consensus strategy to speedup the EKF-based visual SLAM. The proposed algorithm can improve the robustness of the EKF-based visual SLAM and make it operate in real-time. Experimental results validate the proposed algorithm.