천장 영상지도 기반의 전역 위치추정
Global Localization Based on Ceiling Image Map
1
조회
0
다운로드
5
인용
0
참고문헌
키워드
초록
This paper proposes a novel upward-looking camera-based global localization using aceiling image map. The ceiling images obtained through the SLAM process are integrated into theceiling image map using a particle filter. Global localization is performed by matching the ceiling imagemap with the current ceiling image using SURF keypoint correspondences. The robot pose is thenestimated by the coordinate transformation from the ceiling image map to the global coordinate system. A series of experiments show that the proposed method is robust in real environments.
Abstract
This paper proposes a novel upward-looking camera-based global localization using aceiling image map. The ceiling images obtained through the SLAM process are integrated into theceiling image map using a particle filter. Global localization is performed by matching the ceiling imagemap with the current ceiling image using SURF keypoint correspondences. The robot pose is thenestimated by the coordinate transformation from the ceiling image map to the global coordinate system. A series of experiments show that the proposed method is robust in real environments.
참고문헌 (13)
- 장국현, "직선기반 SLAM에서의 루프결합", 로봇학회 논문지, 7(2), pp.120~128 (2012), 한국로봇학회[DOI]
- 남창주, "광범위 환경에서 EKF-SLAM의 일관성 향상을 위한 새로운 관찰모델", 로봇학회 논문지, 7(1), pp.29~34 (2012), 한국로봇학회
- S. Thrun, "Robust Monte Carlo Localization for Mobile Robot", Artificial Intelligence, 128(1), pp.99~141 (1999)
- S. Se, "Vision-based Global Localization and Mapping for Mobile Robots", IEEE Trans. Robotics, 21(3), pp.364~375 (2005)
- M. K. Jung, "Ceiling Image-based Global Localization Using SIFT and ICP Matching", The Annual Fall Conference of Korean Society of Mechanical Engineers, pp.1171~1173 (2013)
- S. Thrun, "MINERVA: A Second-generation Museum Tour-guide Robot", IEEE Proc. International Conference on Robotics and Automation (1999)
- R. Rusinkiewicz, "Efficient Variants of the ICP Algorithm", International Conference on 3D Digital Imaging and Modeling, pp.145~153 (2001)
- Mosaicing a Large Number of widely Dispersed, Noisy, and Distorted Images: A Bayesian Approach, Technical report, "F. Dellaert", computer Science Department, Carnegie Mellon University, ~ (1999)
- An Improved Particle Filter for Non-linear Problems, Tech. Rep., "J. Carpenter", Department of Engineering, University of Cambridge, ~ (1998)
- H. Bay, "Speeded-Up Robust Features (SURF)", Computer Vision and Image Understanding, 110(3), pp.346~359 (2008)
- M. A. Fischeler, "Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography", Communication of the ACM, 24(6), pp.381~395 (1981)
- R. Hartley, "Multiple View Geometry in Computer Vision" (2000), Cambridge Univ. Press
- T. Beier, "Feature-Based Image Metamorphosis", ACM SIGGRAPH Computer Graphics, 26(2), pp.35~42 (1992)