KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
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Robust Features and Accurate Inliers Detection Framework: Application to Stereo Ego-motion Estimation

  • MIN, Haigen (College of Information Engineering, University of Chang'an) ;
  • ZHAO, Xiangmo (College of Information Engineering, University of Chang'an) ;
  • XU, Zhigang (College of Information Engineering, University of Chang'an) ;
  • ZHANG, Licheng (College of Information Engineering, University of Chang'an)
  • Received : 2016.03.15
  • Accepted : 2016.11.28
  • Published : 2017.01.31
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Abstract

In this paper, an innovative robust feature detection and matching strategy for visual odometry based on stereo image sequence is proposed. First, a sparse multiscale 2D local invariant feature detection and description algorithm AKAZE is adopted to extract the interest points. A robust feature matching strategy is introduced to match AKAZE descriptors. In order to remove the outliers which are mismatched features or on dynamic objects, an improved random sample consensus outlier rejection scheme is presented. Thus the proposed method can be applied to dynamic environment. Then, geometric constraints are incorporated into the motion estimation without time-consuming 3-dimensional scene reconstruction. Last, an iterated sigma point Kalman Filter is adopted to refine the motion results. The presented ego-motion scheme is applied to benchmark datasets and compared with state-of-the-art approaches with data captured on campus in a considerably cluttered environment, where the superiorities are proved.

Keywords

References

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