Extended Kalman Filter Based Pose Estimation Using Multiple Cameras

In this work, we solve the pose estimation problem for robot motion by placing multiple cameras on the robot. In particular, we combine the Extended Kalman Filter (EKF) with the multiple cameras. An essential strength of our approach is that it does not require finding image feature correspondences among cameras which is a difficult classical problem. The initial pose, the tracked features, and their corresponding 3D reconstruction are fed to the multiple-camera EKF which estimates the real-time pose. The reason for using multiple cameras is that the pose estimation problem is more constrained for multiple cameras than for a single camera, which has been verified by simulations and real experiments alike. Different approaches using single and two cameras have been compared, as well as two different triangulation methods for the 3D reconstruction. Both the simulations and the real experiments show that our approach is fast, robust and accurate.

[1]  Narendra Ahuja,et al.  Optimal motion and structure estimation , 1989, Proceedings CVPR '89: IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[2]  Xinhua Zhuang,et al.  Pose estimation from corresponding point data , 1989, IEEE Trans. Syst. Man Cybern..

[3]  R. Chellappa,et al.  Recursive 3-D motion estimation from a monocular image sequence , 1990 .

[4]  Xinhua Zhuang,et al.  Robust 3D-3D pose estimation , 1993, 1993 (4th) International Conference on Computer Vision.

[5]  Yaakov Bar-Shalom,et al.  Estimation and Tracking: Principles, Techniques, and Software , 1993 .

[6]  John B. Moore,et al.  Gradient flow approach for pose estimation of quadratic surface [robotics control] , 1995, Proceedings of IECON '95 - 21st Annual Conference on IEEE Industrial Electronics.

[7]  David G. Lowe,et al.  Rigidity checking of 3D point correspondences under perspective projection , 1995, Proceedings of IEEE International Conference on Computer Vision.

[8]  Alex Pentland,et al.  Recursive Estimation of Motion, Structure, and Focal Length , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Michael Isard,et al.  Contour Tracking by Stochastic Propagation of Conditional Density , 1996, ECCV.

[10]  Richard Szeliski,et al.  Shape Ambiguities in Structure From Motion , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Emanuele Trucco,et al.  Introductory techniques for 3-D computer vision , 1998 .

[12]  Autonomous dirigible navigation using visual tracking and pose estimation , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[13]  N. Navab,et al.  Tracking and pose estimation for computer assisted localization in industrial environments , 2000, Proceedings Fifth IEEE Workshop on Applications of Computer Vision.

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

[15]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[16]  Shree K. Nayar,et al.  A general imaging model and a method for finding its parameters , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[17]  Vincenzo Lippiello,et al.  Position and orientation estimation based on Kalman filtering of stereo images , 2001, Proceedings of the 2001 IEEE International Conference on Control Applications (CCA'01) (Cat. No.01CH37204).

[18]  Robert Pless,et al.  A spherical eye from multiple cameras (makes better models of the world) , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[19]  David W. Capson,et al.  Direct visual servoing using network-synchronized cameras and Kalman filter , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[20]  Stefano Soatto,et al.  Structure from Motion Causally Integrated Over Time , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  Geovany de Araújo Borges,et al.  Optimal mobile robot pose estimation using geometrical maps , 2002, IEEE Trans. Robotics Autom..

[22]  Norihiko Itoh,et al.  A support system for maintenance training by augmented reality , 2003, 12th International Conference on Image Analysis and Processing, 2003.Proceedings..

[23]  David Nistér,et al.  An efficient solution to the five-point relative pose problem , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[24]  Robert Pless,et al.  Using many cameras as one , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[25]  Trevor Darrell,et al.  Adaptive view-based appearance models , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[26]  Frank Dellaert,et al.  A multi-camera 6-DOF pose tracker , 2004, Third IEEE and ACM International Symposium on Mixed and Augmented Reality.

[27]  Paul A. Beardsley,et al.  Sequential Updating of Projective and Affine Structure from Motion , 1997, International Journal of Computer Vision.

[28]  Mathias Kölsch,et al.  Emerging Topics in Computer Vision , 2004 .

[29]  Bo Zhang,et al.  Vision based real-time pose estimation for intelligent vehicles , 2004, IEEE Intelligent Vehicles Symposium, 2004.

[30]  Ian D. Reid,et al.  Active tracking of foveated feature clusters using affine structure , 1996, International Journal of Computer Vision.

[31]  Mohan M. Trivedi,et al.  Robust real-time detection, tracking, and pose estimation of faces in video streams , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[32]  Sing Bing Kang,et al.  Emerging Topics in Computer Vision , 2004 .

[33]  Takeo Kanade,et al.  Shape and motion from image streams under orthography: a factorization method , 1992, International Journal of Computer Vision.

[34]  Wen-Yan Chang,et al.  Pose estimation for multiple camera systems , 2004, ICPR 2004.

[35]  Kin Hong Wong,et al.  Recursive three-dimensional model reconstruction based on Kalman filtering , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[36]  Subhashis Banerjee,et al.  Recognizing large isolated 3-D objects through next view planning using inner camera invariants , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[37]  A. Shademan,et al.  Sensitivity analysis of EKF and iterated EKF pose estimation for position-based visual servoing , 2005, Proceedings of 2005 IEEE Conference on Control Applications, 2005. CCA 2005..

[38]  Daniel G. Aliaga,et al.  Robust Bundle Adjustment for Structure from Motion , 2006, 2006 International Conference on Image Processing.

[39]  CamerasAn,et al.  Ego-Motion Estimation Using Optical Flow Fields Observed fromMultiple , 2007 .