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

  • Liang Wang
  • Rong Liu
  • Chao Liang
  • Fuqing Duan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8008)


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.


Feature Point Extend Kalman Filter Feature Point Extraction From Accelerate Segment Test Binary Robust Independent Elementary Feature 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Liang Wang
    • 1
  • Rong Liu
    • 2
  • Chao Liang
    • 1
  • Fuqing Duan
    • 3
  1. 1.College of Electronic Information and Control EngineeringBeijing University of TechnologyBeijingChina
  2. 2.Base DepartmentBeijing Institute of Fashion TechnologyBeijingChina
  3. 3.College of Information Science and TechnologyBeijing Normal UniversityBeijingChina

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