Journal article Open Access

Estimation of Relative Self-Localization Based On Natural Landmark and an Improved SURF

Xing Xiong; Byung-Jae Choi

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.

Files (420.0 kB)
Name Size
420.0 kB Download
  • 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.
  • David G. Lowe, "Distinctive Image Features from Scale-Invariant Keypoints", International Journal of Computer Vision, 2004.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
All versions This version
Views 00
Downloads 00
Data volume 0 Bytes0 Bytes
Unique views 00
Unique downloads 00


Cite as