Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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Omni-directional Vision SLAM using a Motion Estimation Method based on Fisheye Image

어안 이미지 기반의 움직임 추정 기법을 이용한 전방향 영상 SLAM

  • Choi, Yun Won (Automotive IT Platform Research Team, ETR) ;
  • Choi, Jeong Won (Department of Automatic Electrical Engineering, Yeungnam College of Science & Technology) ;
  • Dai, Yanyan (Department of Electrical Engineering, Yeungnam University) ;
  • Lee, Suk Gyu (Department of Electrical Engineering, Yeungnam University)
  • Received : 2014.02.05
  • Accepted : 2014.06.03
  • Published : 2014.08.01
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Abstract

This paper proposes a novel mapping algorithm in Omni-directional Vision SLAM based on an obstacle's feature extraction using Lucas-Kanade Optical Flow motion detection and images obtained through fish-eye lenses mounted on robots. Omni-directional image sensors have distortion problems because they use a fish-eye lens or mirror, but it is possible in real time image processing for mobile robots because it measured all information around the robot at one time. In previous Omni-Directional Vision SLAM research, feature points in corrected fisheye images were used but the proposed algorithm corrected only the feature point of the obstacle. We obtained faster processing than previous systems through this process. The core of the proposed algorithm may be summarized as follows: First, we capture instantaneous $360^{\circ}$ panoramic images around a robot through fish-eye lenses which are mounted in the bottom direction. Second, we remove the feature points of the floor surface using a histogram filter, and label the candidates of the obstacle extracted. Third, we estimate the location of obstacles based on motion vectors using LKOF. Finally, it estimates the robot position using an Extended Kalman Filter based on the obstacle position obtained by LKOF and creates a map. We will confirm the reliability of the mapping algorithm using motion estimation based on fisheye images through the comparison between maps obtained using the proposed algorithm and real maps.

Keywords

References

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