동적 환경에 강인한 장면 인식 기반의 로봇 자율 주행
Scene Recognition based Autonomous Robot Navigation robust to Dynamic Environments
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초록
Recently, many vision-based navigation methods have been introduced as an intelligent robot application. However, many of these methods mainly focus on finding an image in the database corresponding to a query image. Thus, if the environment changes, for example, objects moving in the environment, a robot is unlikely to find consistent corresponding points with one of the database images. To solve these problems, we propose a novel navigation strategy which uses fast motion estimation and a practical scene recognition scheme preparing the kidnapping problem, which is defined as the problem of re-localizing a mobile robot after it is undergone an unknown motion or visual occlusion. This algorithm is based on motion estimation by a camera to plan the next movement of a robot and an efficient outlier rejection algorithm for scene recognition. Experimental results demonstrate the capability of the vision-based autonomous navigation against dynamic environments.
Abstract
Recently, many vision-based navigation methods have been introduced as an intelligent robot application. However, many of these methods mainly focus on finding an image in the database corresponding to a query image. Thus, if the environment changes, for example, objects moving in the environment, a robot is unlikely to find consistent corresponding points with one of the database images. To solve these problems, we propose a novel navigation strategy which uses fast motion estimation and a practical scene recognition scheme preparing the kidnapping problem, which is defined as the problem of re-localizing a mobile robot after it is undergone an unknown motion or visual occlusion. This algorithm is based on motion estimation by a camera to plan the next movement of a robot and an efficient outlier rejection algorithm for scene recognition. Experimental results demonstrate the capability of the vision-based autonomous navigation against dynamic environments.
참고문헌 (20)
- D. Burschka, "Vision-based control of mobile robots", IEEE International Conference on Robotics and Automation (2001)
- Stepha Se, "Mobile Robot Localization and Mapping with Uncertainty using Scale-Invariant Visual Landmarks", International Journal of Robotics Research, 21(8) (2002)
- Yasushi Yagi, "Iconic Memory-Based Omnidirectional Route Panorama Navigation", IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(1) (2006)
- Jose Gaspar, "Vision-based Navigation and Environment Representations with an Omni-directional Camera", IEEE Transactions on Robotics and Automation, 16(6) (2000)
- Zhichao Chen, "Qualitative Vision-based Mobile Robot Navigation", IEEE International Conference on Robotics and Automation (2006)
- O.Booij, "Navigation using an appearance based topological map", IEEE International Conference on Robotis and Automation (2007)
- Friedrich Faundorfer, "Topological mapping, localization and navigationnavigation using image collection", IEEE, 2007, ~
- Sinsa Segvic, "Large scale vision-based navigation without an accurate global reconstruction", IEEE Conference on Computer Vision and Pattern Recognition (2007)
- D. Lowe, "Distinctive image features from scaleinvariant Keypoints", International Journal of Computer Vision, 60(2) (2004)
- J.Matas, O.Chum, "Robust wide baseline stereo from maximally stable extremal regions", British Machine Vision Conference (2002)
- Sungho Kim, "Biologically Motivated Perceptual Feature : Generalized Robust Invariant Feature", LNCS, 3852, pp.350~364 (2006)
- C.Harris, "A combined corner and edge detector", In Alvey Vision Conference, pp.147~152 (1988)
- Jungho Kim, "Robust Feature Matching for Loop Closing and Localization", IEEE, 2007, ~
- Jianbo Shi, "Good Features to Track", IEEE Conference on Computer Vision and Pattern Recognition (1994)
- David Noster, "Visual Odometry", IEEE Conference on Computer Vision and Pattern Recognition (2004)
- R.Haralick, "Review and Analysis of Solutions of the Three Point Perspective Pose Estimation Problem", International Journal of Computer Vision, 13(3) (1994)
- David Nister, "A Minimal Solution to the Generalized 3-Point Pose Problem", IEEE Conference on Computer Vision and Pattern Recongition (2004)
- R. Hartley, "Multiple View Geometry in Computer Vision" (2000), Cambridge University Press
- Jungho Kim, "Efficient Feature Tracking for Scene Recongition using Angular and Scale Constraints", IEEE, 2008, ~
- Marius Leordeanu, "A Spectral Technique for Correspondence Problems Using Pairwise Constraints", IEEE International Conference on Computer Vision (2005)