3D-objecttracking with a mixed omnidirectional stereo camera system

This paper presents a novel technique of stereo vision based on the combination of an omnidirectional camera and a perspective camera. The technique combines the 360° field of view of the omnidirectional camera with the long field of view of a perspective camera. We describe the setup of such a camera system and how it can be used to achieve 3D-position estimates. Furthermore, we develop a maximum likelihood approach and a Bayesian approach that are able to fuse monocular and binocular observations of the same object to estimate its position and movement and show how this technique can be applied successfully in the RoboCup MiddleSizeLeague.

[1]  Hiroaki Kitano,et al.  RoboCup: A Challenge Problem for AI , 1997, AI Mag..

[2]  W. Wong,et al.  The calculation of posterior distributions by data augmentation , 1987 .

[3]  P. Sturm Mixing catadioptric and perspective cameras , 2002, Proceedings of the IEEE Workshop on Omnidirectional Vision 2002. Held in conjunction with ECCV'02.

[4]  Thia Kirubarajan,et al.  Estimation with Applications to Tracking and Navigation: Theory, Algorithms and Software , 2001 .

[5]  Martin Lauer,et al.  Cognitive concepts in autonomous soccer playing robots , 2010, Cognitive Systems Research.

[6]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[7]  Thiagalingam Kirubarajan,et al.  Comparison of EKF, pseudomeasurement, and particle filters for a bearing-only target tracking problem , 2002, SPIE Defense + Commercial Sensing.

[8]  Gm Gero Walter,et al.  Bayesian linear regression , 2009 .

[9]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Naokazu Yokoya,et al.  Generation of high-resolution stereo panoramic images by omnidirectional imaging sensor using hexagonal pyramidal mirrors , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[11]  Branko Ristic,et al.  Tracking a manoeuvring target using angle-only measurements: algorithms and performance , 2003, Signal Process..

[12]  A. Zelinsky,et al.  Stereo panoramic vision for monitoring vehicle blind-spots , 2004, IEEE Intelligent Vehicles Symposium, 2004.

[13]  Martin Lauer,et al.  Real-time 3D Ball Recognition using Perspective and Catadioptric Cameras , 2007, EMCR.

[14]  Tomás Svoboda,et al.  Epipolar Geometry for Central Catadioptric Cameras , 2002, International Journal of Computer Vision.

[15]  Xiao-Li Meng,et al.  Maximum likelihood estimation via the ECM algorithm: A general framework , 1993 .

[16]  Branko Ristic,et al.  Bearings-Only Tracking of Manoeuvring Targets Using Particle Filters , 2004, EURASIP J. Adv. Signal Process..

[17]  Richard Szeliski,et al.  A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms , 2001, International Journal of Computer Vision.

[18]  Greg Welch,et al.  An Introduction to Kalman Filter , 1995, SIGGRAPH 2001.

[19]  J. J. Guerrero,et al.  Matching of omnidirectional and perspective images using the hybrid fundamental matrix , 2008 .

[20]  Anup Basu,et al.  Panoramic stereo reconstruction using non-SVP optics , 2005, Comput. Vis. Image Underst..

[21]  Shree K. Nayar,et al.  A Theory of Single-Viewpoint Catadioptric Image Formation , 1999, International Journal of Computer Vision.

[22]  Joseph L Schafer,et al.  Analysis of Incomplete Multivariate Data , 1997 .

[23]  Martin Lauer,et al.  Calculating the Perfect Match: An Efficient and Accurate Approach for Robot Self-localization , 2005, RoboCup.

[24]  Donald Geman,et al.  Bayes Smoothing Algorithms for Segmentation of Binary Images Modeled by Markov Random Fields , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.