DOI QR코드

DOI QR Code

GPU 가속화를 통한 이미지 특징점 기반 RGB-D 3차원 SLAM

Image Feature-Based Real-Time RGB-D 3D SLAM with GPU Acceleration

  • 이동화 (KAIST 건설 및 환경공학과) ;
  • 김형진 (KAIST 건설 및 환경공학과) ;
  • 명현 (KAIST 건설 및 환경공학과)
  • Lee, Donghwa (Dept. of Civil and Environmental Engineering, KAIST) ;
  • Kim, Hyongjin (Dept. of Civil and Environmental Engineering, KAIST) ;
  • Myung, Hyun (Dept. of Civil and Environmental Engineering, KAIST)
  • 투고 : 2013.02.28
  • 심사 : 2013.04.02
  • 발행 : 2013.05.01

초록

This paper proposes an image feature-based real-time RGB-D (Red-Green-Blue Depth) 3D SLAM (Simultaneous Localization and Mapping) system. RGB-D data from Kinect style sensors contain a 2D image and per-pixel depth information. 6-DOF (Degree-of-Freedom) visual odometry is obtained through the 3D-RANSAC (RANdom SAmple Consensus) algorithm with 2D image features and depth data. For speed up extraction of features, parallel computation is performed with GPU acceleration. After a feature manager detects a loop closure, a graph-based SLAM algorithm optimizes trajectory of the sensor and builds a 3D point cloud based map.

과제정보

연구 과제번호 : 실회환경에 강인한 도로 기반 저가형 자율주행기술 개발

연구 과제 주관 기관 : 지식경제부

참고문헌

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  12. P. Henry, M. Krainin, E. Herbst, X. Ren, and D. Fox, "RGB-D mapping: Using Kinect-styledepth cameras for dense 3D modeling of indoor environments," The International Journal of Robotics Research, vol. 31, no. 5, pp. 647-663, Apr. 2012. https://doi.org/10.1177/0278364911434148
  13. D. Lee, H. Kim, and H.Myung, "Real-time RGB-D 3D SLAM with GPU acceleration," Proc. of Daejeon & Chungcheong Regional Conferenceof Institute of Control, Robotics and Systems (in Korean), pp. 179-182, Dec. 2012.
  14. D. Lee, H.Kim, and H. Myung, "2D image feature-based real-time RGB-D 3D SLAM," Proc. of International Conference on Robot Intelligence Technology and Applications 2012 (RiTA 2012), pp. 485-492, Gwangju, Korea, Dec. 2012.
  15. D. Lee, H.Kim, and H.Myung, "GPU-based real-time RGB-D 3D SLAM," Proc. of International Conference on Ubiquitous Robots and Ambient Intelligence 2012 (URAI 2012), pp. 46-48, Daejeon, Korea, Nov. 26-29, 2012.
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  18. OpenCV (Open source Computer Vision): http://opencv.org/
  19. PCL (Point Cloud Library): http://pointclouds.org/

피인용 문헌

  1. Localization of a Monocular Camera using a Feature-based Probabilistic Map vol.21, pp.4, 2015, https://doi.org/10.5302/J.ICROS.2015.14.8035
  2. 3D Omni-directional Vision SLAM using a Fisheye Lens Laser Scanner vol.21, pp.7, 2015, https://doi.org/10.5302/J.ICROS.2015.15.0002
  3. Obstacle Detection Algorithm Using Forward-Viewing Mono Camera vol.21, pp.9, 2015, https://doi.org/10.5302/J.ICROS.2015.15.0104