On-Line Identification of Line Segments Using Hypothesis Testing for Robot Mapping

Guang Lei Huo; Li Jun Zhao; Rui Feng Li; Ke Wang; Ming Rui Lv

doi:10.4028/www.scientific.net/AMR.902.245

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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 902On-Line Identification of Line Segments Using...

On-Line Identification of Line Segments Using Hypothesis Testing for Robot Mapping

Abstract:

A novel features identification method is proposed for mobile robot indoor environment perception. In this paper, firstly, multi-dimensional data space is building based on laser scanner data, in which corner point is located by hypothesis testing theory. Secondly, line segments are obtained and localized by fitting method, which are previously estimated using probability model. At last, experiments verify the efficiency of proposed algorithm in indoor environment.

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Periodical:

Advanced Materials Research (Volume 902)

Pages:

245-249

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.902.245

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Online since:

February 2014

Authors:

Guang Lei Huo, Li Jun Zhao*, Rui Feng Li, Ke Wang, Ming Rui Lv

Keywords:

Hypothesis Testing, Mapping, Robot Localization, Segment Features

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* - Corresponding Author

References

[1] Leonard J, How J，Teller S，et a1．A perception-driven autonomous urban vehicle[J]．Journal of Field Robotics, 2008. 25(10): 727—774.

Google Scholar

[2] Li Yangming, Meng Qinghu. A General-purpose Method to Extract Features from LIDAR Data[J]. Robot, 2010, 32(6): 812-821.

Google Scholar

[3] Madhavan R, Durrant-Whyte H F. Natural landmark-based autonomous vehicle navigation[J]. Robotics and Autonomous Systems, 2004, 46(2): 79-95.

DOI: 10.1016/j.robot.2003.11.003

Google Scholar

[4] Borges G A, Aldon M J. A split-and-merge segmentation algorithm for line extraction in 2D range images [C]. Rroceedings of 15th International Conference on Pattern Recognition. Los Alamitos USA: IEEE Computer Society, 2000: 441-444.

DOI: 10.1109/icpr.2000.905371

Google Scholar

[5] M. Fischler and R. Bolles. Random sample consensus: A paradigm for model fitting with application to image analysis and automated cartography[J]. Communications of the ACM, 1981, 24(6): 381–395.

DOI: 10.1145/358669.358692

Google Scholar

[6] Bayro-Corrochano, E, Bernal-Marin, M. Generalized hough transform and conformal geometric algebra to detect lines and planes for building 3D maps and robot navigation[C]. Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, Taipei, 2010: 810-815.

DOI: 10.1109/iros.2010.5651289

Google Scholar