Toward image-based localization for AIBO using wavelet transform Department of Information Engineering University of Padua, Italy A. Pretto, E. Menegatti, E. Pagello, Y. Jitsukawa, R. Ueda, T. Arai Dept. of Precision Engineering University of Tokyo, Japan Abstract This paper describes a similarity measure to be used in image-based localization by autonomous robots with low computational resources. We propose a novel signature which allows memory saving and fast similarity calculation. The signature is based on the calculation of the 2D Haar Wavelet Transform of the gray-level image. We present experiments showing the effectiveness of the proposed image similarity measure. The used images were collected using the AIBOs ERS-7 of the RoboCup Team Araibo of the University of Tokyo on a RoboCup field. However, the proposed image similarity measure does not use any information on the structure of the environment and do not exploit the peculiar features of the RoboCup environment. Introduction Any techniques of image-based localization has to solve: (i)how to reduce the number of images necessary to fully describe the environment; (i)how to efficiently store a large data set of reference images (it is common to have several hundred reference images for typical environments); (ii)how to calculate in a fast and efficient way the similarity Each of the referred works addressed one (or all) of these problems. References 1. R. Cassinis, D. Duina, S. Inelli, and A. Rizzi. Unsupervised matching of visual landmarks for robotic homing using Fourier-Mellin transform. Robotics and Autonomous Systems, 40(2-3), August M. N. Do and M. Vetterli. Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance. IEEE Tr. Im. Proc., 11(2):146–158, February E. Frontoni, P. Zingaretti. An efficient similarity metric for omnidirectional vision sensors. Robotics and Autonomous Systems, 54(9):750–757, J. Gaspar, N. Winters, and J. Santos-Victor. Vision-based navigation and environmental representations with an omnidirectional camera. IEEE Transaction on Robotics and Automation, Vol 16(number 6), Dec H.-M. Gross, A. Koenig, Ch. Schroeter, and H.-J. Boehme. Omnivision-based prob-abilistic self-localization for a mobile shopping assistant continued. In IEEE/RSJ Int. Conference on Intelligent Robots and Systems (IROS 2003), pages pp. 1505–1511, October 2003, Las Vegas USA. 11. B.J.A. Koerse, N. Vlassis, R. Bunschoten, and Y. Motomura. A probabilistic model for appareance-based robot localization. Image and Vision Computing, vol.19(6):pp. 381–391, April Emanuele Menegatti, Takeshi Maeda, and Hiroshi Ishiguro. Image-based memory for robot navigation using properties of the omnidirectional images. Robotics and Autonomous Systems, Elsevier, 47(4):pp. 251–267, July M. Vetterli and J Kovacevic. Wavelets and Subband Coding. Signal Processing Series J. Wolf, W. Burgard, and H. Burkhardt. Robust vision-based localization by combining an image retrieval system with monte carlo localization. IEEE Transactions on Robotics, 21(2):208–216, Our approach To minimize the reference images to be stored, we keep as reference images two 180 degree panoramic views of the environment at every reference location We developed an algorithm that allows the ERS-7 robot to autonomously build two 180 degrees panorama images using its standard camera and to stitch them together. We use as image signature a 2-D Haar Wavelet Transform of the grey-level values of the image. We decide to stop at 4-th level decomposition, and to characterize images only by the detailed coefficients (horizontal, vertical and diagonal) of this level. The problem How to implement an image-based localization approach on a robot with limited storage memory and limited computational resources, as the Sony AIBO ERS-7? How to store in a memory-saving way the reference images? How to efficiently compare them with the input images? Which feature should we relay on, for a general approach not targeted to a particular environment? Wavelet signature We use as image signature a 2-D Haar Wavelet Transform of the grey-level values of the image. We decide to stop at 4-th level decomposition, and to characterize images only by the detailed coefficient (horizontal, vertical and diagonal) of this level. Haar Wavelet because: very effective in detecting discontinuity (one of the most important features in image-based localization) easily implemented and very fast to compute If one is interested in image reconstruction phase, the Haar Wavelets are not the good choice, because they tend to produce a lot of squared artifacts Memory saving: a gray-scale omnidirectional image 720x160 = Kbyte ==> only 1.3 Kbyte. Experiments We tested the system in a RoboCup Four-legged League 540x360 cm soccer field, using a grid of 13 by 9 reference images. However, the proposed systems do not relay on RoboCup features - 90° +90° Input Image best match ==> second best match ==> <== Input Images <== resulting probability distribution 2-D Haar Wavelet