Robust tracking and structure from motion with sample based uncertainty representation

Geometric reconstruction of the environment from images is critical in autonomous mapping and robot navigation. Geometric reconstruction involves feature tracking, i.e., locating corresponding image features in consecutive images, and structure from motion (SFM), i.e., recovering the 3D structure of the environment from a set of correspondences between images. Although algorithms for feature tracking and structure from motion are well-established, their use in practical mobile robot applications is still difficult because of occluded features, non-smooth motion between frames, and ambiguous patterns in images. We show how a sampling-based representation can be used in place of the traditional Gaussian representation of uncertainty. We show how sampling can be used for both feature tracking and SFM and we show how they are combined in this framework. The approach is exercised in the context of a mobile robot navigating through an outdoor environment with an omnidirectional camera.

[1]  Wolfram Burgard,et al.  Monte Carlo localization for mobile robots , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[2]  Philip H. S. Torr,et al.  The Development and Comparison of Robust Methods for Estimating the Fundamental Matrix , 1997, International Journal of Computer Vision.

[3]  M. Hebert,et al.  Omni-directional structure from motion , 2000, Proceedings IEEE Workshop on Omnidirectional Vision (Cat. No.PR00704).

[4]  David W. Murray,et al.  A unifying framework for structure and motion recovery from image sequences , 1995, Proceedings of IEEE International Conference on Computer Vision.

[5]  Peter I. Corke,et al.  A tutorial on visual servo control , 1996, IEEE Trans. Robotics Autom..

[6]  Berthold K. P. Horn,et al.  Direct methods for recovering motion , 1988, International Journal of Computer Vision.

[7]  Bobby Rao,et al.  Data association methods for tracking systems , 1993 .

[8]  Rama Chellappa,et al.  Structure from Motion Using Sequential Monte Carlo Methods , 2004, International Journal of Computer Vision.

[9]  Michael J. Black,et al.  Robust dynamic motion estimation over time , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[10]  Andrew Blake,et al.  A Probabilistic Exclusion Principle for Tracking Multiple Objects , 2000, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[11]  Michael Isard,et al.  CONDENSATION—Conditional Density Propagation for Visual Tracking , 1998, International Journal of Computer Vision.

[12]  David J. Fleet,et al.  Stochastic Tracking of 3D Human Figures Using 2D Image Motion , 2000, ECCV.

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

[14]  David A. Forsyth,et al.  Bayesian structure from motion , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[15]  Ieee Robotics,et al.  IEEE journal of robotics and automation , 1985 .

[16]  Hugh Griffiths,et al.  Radar, Sonar and Navigation , 2002 .

[17]  Pietro Perona,et al.  Visual navigation using a single camera , 1995, Proceedings of IEEE International Conference on Computer Vision.

[18]  Larry H. Matthies,et al.  Error modeling in stereo navigation , 1986, IEEE J. Robotics Autom..

[19]  Pietro Perona,et al.  Motion Estimation on the Essential Manifold , 1994, ECCV.

[20]  P. Fearnhead,et al.  Improved particle filter for nonlinear problems , 1999 .

[21]  Kevin Nickels,et al.  Measurement error estimation for feature tracking , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[22]  David J. C. Mackay,et al.  Introduction to Monte Carlo Methods , 1998, Learning in Graphical Models.