Estimation of Relative Self-Localization Based On Natural Landmark and an Improved SURF
It is important for an autonomous mobile robot to know
where it is in any time in an indoor environment. In this paper, we
design a relative self-localization algorithm. The algorithm compare
the interest point in two images and compute the relative displacement
and orientation to determent the posture. Firstly, we use the SURF
algorithm to extract the interest points of the ceiling. Second, in order
to reduce amount of calculation, a replacement SURF is used to extract
orientation and description of the interest points. At last, according to
the transformation of the interest points in two images, the relative
self-localization of the mobile robot will be estimated greatly.
[1] David G. Lowe, "Distinctive Image Features from Scale-Invariant
Keypoints", International Journal of Computer Vision, 2004.
[2] Herbert Bay, Tinne Tuytelaars, and Luc Van Gool, "SURF: Speeded Up
Robust Features", Computer Vision and Image Understanding (CVIU),
Vol. 110, No. 3, pp. 346-359, 2008.
[3] Y. Ke and R. Sukthankar.PCA-SIFT: "A More Distinctive Representation
for Local Image Descriptors", Proc. Conf. Computer Vision and Pattern
Recognition, pp. 511-517, 2004.
[4] David C. K. Yuen and Bruce A. MacDonald: Vision-Based Localization
Algorithm Based on Landmark Matching, Triangulation, Reconstruction,
and Comparison, IEEE Transactions on Robotics, vol. 21, no. 2, pp.
217-226, April. 2005.
[5] Junyi Zhou, Jing Shi and Xiuli Qu: "Statistical characteristics of
landmark-based localization performance", Int J Adv Manuf Technol, vol.
46, 2010, pp.1215-1227.
[6] Luo Juan and Oubong Gwun, "A Comparison of SIFT, PCA-SIFT and
SURF", International Journal of Image Processing (IJIP), Vol. 3, No. 4,
pp. 143-152.
[7] X. Xiong and B. J. Choi, "A Replacement Algorithm of Fast Computing
Interest Point-s Orientation and Descriptor in SURF for Self-localization
Robot",Lecture Notes in Computer Science (LNCS 7425), pp. 339-349,
2012.
[8] De Xu, Liwei Han, Min Tan, and You Fu Li: "Ceiling-Based Visual
Positioning for an Indoor Mobile Robot With Monocular Vision", IEEE
Transactions on Industrial Electronics, Vol. 56, No. 5, 2009, pp.
1617-1628.
[1] David G. Lowe, "Distinctive Image Features from Scale-Invariant
Keypoints", International Journal of Computer Vision, 2004.
[2] Herbert Bay, Tinne Tuytelaars, and Luc Van Gool, "SURF: Speeded Up
Robust Features", Computer Vision and Image Understanding (CVIU),
Vol. 110, No. 3, pp. 346-359, 2008.
[3] Y. Ke and R. Sukthankar.PCA-SIFT: "A More Distinctive Representation
for Local Image Descriptors", Proc. Conf. Computer Vision and Pattern
Recognition, pp. 511-517, 2004.
[4] David C. K. Yuen and Bruce A. MacDonald: Vision-Based Localization
Algorithm Based on Landmark Matching, Triangulation, Reconstruction,
and Comparison, IEEE Transactions on Robotics, vol. 21, no. 2, pp.
217-226, April. 2005.
[5] Junyi Zhou, Jing Shi and Xiuli Qu: "Statistical characteristics of
landmark-based localization performance", Int J Adv Manuf Technol, vol.
46, 2010, pp.1215-1227.
[6] Luo Juan and Oubong Gwun, "A Comparison of SIFT, PCA-SIFT and
SURF", International Journal of Image Processing (IJIP), Vol. 3, No. 4,
pp. 143-152.
[7] X. Xiong and B. J. Choi, "A Replacement Algorithm of Fast Computing
Interest Point-s Orientation and Descriptor in SURF for Self-localization
Robot",Lecture Notes in Computer Science (LNCS 7425), pp. 339-349,
2012.
[8] De Xu, Liwei Han, Min Tan, and You Fu Li: "Ceiling-Based Visual
Positioning for an Indoor Mobile Robot With Monocular Vision", IEEE
Transactions on Industrial Electronics, Vol. 56, No. 5, 2009, pp.
1617-1628.
@article{"International Journal of Information, Control and Computer Sciences:55291", author = "Xing Xiong and Byung-Jae Choi", title = "Estimation of Relative Self-Localization Based On Natural Landmark and an Improved SURF", abstract = "It is important for an autonomous mobile robot to know
where it is in any time in an indoor environment. In this paper, we
design a relative self-localization algorithm. The algorithm compare
the interest point in two images and compute the relative displacement
and orientation to determent the posture. Firstly, we use the SURF
algorithm to extract the interest points of the ceiling. Second, in order
to reduce amount of calculation, a replacement SURF is used to extract
orientation and description of the interest points. At last, according to
the transformation of the interest points in two images, the relative
self-localization of the mobile robot will be estimated greatly.", keywords = "Relative Self-Localization Posture, SURF, Natural Landmark, Interest Point.", volume = "7", number = "1", pages = "83-5", }