이동로봇의 물체인식 기반 전역적 자기위치 추정
Object Recognition-based Global Localization for Mobile Robots
1
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
Based on object recognition technology, we present a new global localization method for robot navigation. For doing this, we model any indoor environment using the following visual cues with a stereo camera; view-based image features for object recognition and those 3D positions for object pose estimation. Also, we use the depth information at the horizontal centerline in image where optical axis passes through, which is similar to the data of the 2D laser range finder. Therefore, we can build a hybrid local node for a topological map that is composed of an indoor environment metric map and an object location map. Based on such modeling, we suggest a coarse-to-fine strategy for estimating the global localization of a mobile robot. The coarse pose is obtained by means of object recognition and SVD based least-squares fitting, and then its refined pose is estimated with a particle filtering algorithm. With real experiments, we show that the proposed method can be an effective vision- based global localization algorithm.
Abstract
Based on object recognition technology, we present a new global localization method for robot navigation. For doing this, we model any indoor environment using the following visual cues with a stereo camera; view-based image features for object recognition and those 3D positions for object pose estimation. Also, we use the depth information at the horizontal centerline in image where optical axis passes through, which is similar to the data of the 2D laser range finder. Therefore, we can build a hybrid local node for a topological map that is composed of an indoor environment metric map and an object location map. Based on such modeling, we suggest a coarse-to-fine strategy for estimating the global localization of a mobile robot. The coarse pose is obtained by means of object recognition and SVD based least-squares fitting, and then its refined pose is estimated with a particle filtering algorithm. With real experiments, we show that the proposed method can be an effective vision- based global localization algorithm.
참고문헌 (23)
- D. Fox, "Markov localization for mobile robots in dynamic environments", Journal of Artificial Intelligence Research, 11, pp.391~427 (1999)
- D. Fox, "Monte Carlo Localization for Mobile Robots", IEEE International Conference on Robotics and Automation, pp.1322~1328 (1999)
- Dongheui Lee, "A Reliable Position Estimation Method of the Service Robot by Map Matching", IEEE International Conference on Robotics and Automation, pp.2830~2835 (2003)
- D. Kriegman, "Stereo Vision and navigation in Buildings for Mobile Robots", IEEE Trans. on Robotics and Automation, 6, pp.792~803 (1989)
- C. Fennema, "Model-directed mobile robot navigation", IEEE Trans. on System Man and Cybernetics, 20(6), pp.1352~1369 (1990)
- S. Atiya, "Real-time vision based robot localization", IEEE Trans. on Robotics and Automation, 9(6), pp.785~800 (1993)
- R. Sim, "Learning and evaluating visual features for pose estimation", IEEE International Conference on Computer Vision, 1(2), pp.1217~1222 (1999)
- Y. Matsumoto, "Visual Navigati-on using View-Sequenced Route Representation", IEEE International Conference on Robotics and Automation, pp.83~88 (1996)
- M. Tomono, "Indoor Navigation based on an Inaccurate Map using Object Recognition", IEEE, 2002, 399~405
- Jana Košecká, "Vision Based Topological Markov Localization", IEEE International Conference on Robotics and Automation, pp.1481~1486 (2004)
- S. Se, D. Lowe, "Mobile Robot Localization and Mapping with Uncertainty using Scale-Invariant Visual Landmarks", The International Journal of Robotics Research, 21(8), pp.735~758 (2002)
- R. F. Wang, "Human spatial representation:insights from animals", Trends in Cognitive Science, 6(9), pp.376~382 (2002)
- W.K.Yeap and M.E.Jefferies, "On early cognitive mapping", Spatial Cognition and Computation, 2, pp.85~116 (2000)
- Sung-Kee Park, "Mobile Robot Navigation Based on Direct Depth and Color-based Environment Modeling", IEEE International Conference on Robotics and Automation, pp.4253~4258 (2004)
- Yan Ke, "PCA-SIFT:A More Distinctive Representation for Local Image Descriptors", IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2, pp.503~513 (2004)
- M. Leordeanu, "Spectral Technique for Correspondence Problems using Pairwise Constraints", IEEE International Conference on Computer Vision, pp.1482~1489 (2005)
- John H.Challis, "A procedure determining rigid body transformation parameters", Journal of Biomechanics, 28(6), pp.733~737 (1995)
- B. Ristic, "Beyond the Kalman Filter – Particle Filters for Tracking Applications" (2004), Artech House
- I.Isard, "CONDENSATION – conditional density propagation for visual tracking", Int. J. Computer Vision, 29(1), pp.5~28 (1998)
- E. Kreyszig, "Advanced Engineering Mathematics" (2006), John Wiley & Sons
- [URL]
- J.-S. Gutmann, "An experimental comparison of localization methods", IEEE, 1998, 736~743
- D. Fox, "Monte Carlo localization: Efficient position estimation for mobile robots", National Conference on Artificial Intelligence, pp.896~901 (1999)