Research on the effect of image size on real-time performance of robot vision positioning
Tóm tắt
In order to improve the real-time performance of visual positioning of the indoor mobile robot, the researchers found that the shape and size of the positioned image have a great influence on the real-time performance of the positioning calculation. In order to verify the conclusion and find the appropriate image shape and size to meet the robot’s visual positioning requirements, this paper adopts four different shapes, such as quadrilateral and circular, and uses SURF algorithm to extract and recognize the features of the image. The effect of image shape and size on real-time localization is studied from two aspects: the localization of different shape models under the same size by the visual robot and the localization of the different shape models by the visual robot. It is found that the accuracy and real time of positioning squares and circles are higher than the accuracy and real time of positioning triangles and hexagons under the same size. And when the image size ratio is between 40 and 60% of the original image, the change of the number of feature points is relatively stable and the number of feature points is moderate. It can improve the real-time performance of mobile robot vision localization under the premise of a certain positioning accuracy.
Tài liệu tham khảo
De Xu, Robot Vision Measurement and Control[M]. Second Edition. Beijing: National Defense Industry Press,2011
C. Schmid, R. Mohr, Local grayvalue invariants for image retrieval[J]. IEEE Trans. Pattern Anal. Mach. Intell 19(5), 530–535 (1997)
K. Mikolajczyk, C. Schmid, Indexing based on scale invariant interest points[J]. Proc.Int.Conf.Comput.Vis. Vancouver Canada, 1 (2010)
K. Mikolajczyk, C. Schmid, Scale & affine invariant interest point detectors[J]. Int. J. Comput. Vis 60(1), 63–86 (2004)
Lowe D G. Object Recognition from Local Scale-Invariant Features[C]. In: Proceedings of the Seventh IEEE International Conference on Computer Vision (IEEE, 1999), pp. 1150–1176
Mikolajczyk K, Schmid C. A performance evaluation of local descriptors[C]. IEEE Trans. 27(10), 257 (2013)
H. Bay, A. Ess, T. Tuytelaars, et al., Speeded-up robust features[J]. Comput. Vis. Image Underst 110(3), 404–417 (2008)
X.J. Liu, J. Yang, J.W. Sun, et al., Image registration approach based on SIFT[J]. Infrared Laser Eng 37(1), 156–160 (2008)
L. Jia, W. Fu, W. Wen, et al., Image matching based on improved SIFT algorithm[J]. Chin. J. Sci. Instrum 34(5), 1107–1112 (2013)
Li B, Liu L, Wei Z Q. A strong robust real-time image matching algorithm[J]. Journal of Software. (7), 1583–1592 (2014)
Wang G, Yu J, Hou Y. An image registration algorithm based on features point and cluster[C]. International Congress on Image and Signal Processing IEEE. 836–840 2013
C.J. Harris, A combined corner and edge detector[J]. Proc. Alvey Vis. Conf 1988(3), 147–151 (1988)
C. Jian, L.I. Kan, C.X. Gao, et al., Application of local features in aerial image mosaic[J]. J. Univ. Electron. Sci. Technol. China 42(1), 125–129 (2013)
Ke Y, Sukthankar R. PCA-SIFT: a more distinctive representation for local image descriptors[C]// Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on. IEEE. 506–513 (2004)
Han L I, Niu J Z, Guo H. Automatic seamless image mosaic method based on feature points[J]. Computer Engineering & Design 28(9), 2083–2085 (2007)
M. Pateraki, H. Baltzakis, P. Trahanias, Visual estimation of pointed targets for robot guidance via fusion of face pose and hand orientation ☆[J]. Comput. Vis. Image. Underst 120(5), 1–13 (2013)
P.F. Alcantarilla, L.M. Bergasa, et al., Gauge-SURF descriptors[J]. Image. Vis. Comput 31(1), 103–116 (2013)
F. Bellavia, D. Tegolo, C. Valenti, Keypoint descriptor matching with context-based orientation estimation ☆[J]. Image. Vis. Comput 32(9), 559–567 (2014)
H. Mehrotra, P.K. Sa, B. Majhi, Fast segmentation and adaptive SURF descriptor for iris recognition[J]. Math. Comput. Model 58(1–2, 132), –146 (2013)
D.G. Lowe, Distinctive image features from scale-invariant keypoints[J]. Int. J. Comput. Vis. 60(2), 91–110 (2004)
Grabner M, Grabner H, Bischof H. Fast approximated SIFT[C]. Asian Conference on Computer Vision. (Hyderabad, Springer-Verlag, 2006), pp. 918–927