Visual odometry from an omnidirectional vision system

We describe a method for estimating the translation and rotation between two subsequent poses of a moving robot from images taken with an omnidirectional vision system. This allows some form of visual odometry. We use two sorts of projections derived from the omnidirectional image. The rotation and translation direction are determined from panoramic projections. After that, a projection on a plane parallel to the ground is used to estimate the length of the translation vector. Experiments on real and simulated data are carried out.

[1]  Richard I. Hartley,et al.  Estimation of Relative Camera Positions for Uncalibrated Cameras , 1992, ECCV.

[2]  Carlo Tomasi,et al.  Good features to track , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[3]  R. Hartley.  In Defence of the &point Algorithm , 1995 .

[4]  Richard I. Hartley,et al.  In defence of the 8-point algorithm , 1995, Proceedings of IEEE International Conference on Computer Vision.

[5]  Takeo Kanade,et al.  Development of a video-rate stereo machine , 1995, Proceedings 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human Robot Interaction and Cooperative Robots.

[6]  Richard Szeliski,et al.  Video mosaics for virtual environments , 1996, IEEE Computer Graphics and Applications.

[7]  Bart M. ter Haar Romeny.  Applications of Scale-Space Theory , 1997, Gaussian Scale-Space Theory.

[8]  Zhengyou Zhang,et al.  Parameter estimation techniques: a tutorial with application to conic fitting , 1997, Image Vis. Comput..

[9]  Shree K. Nayar,et al.  A theory of catadioptric image formation , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[10]  Harpreet S. Sawhney,et al.  Robust Video Mosaicing through Topology Inference and Local to Global Alignment , 1998, ECCV.

[11]  Frank Dellaert,et al.  Structure from motion without correspondence , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[12]  Ben J. A. Kröse,et al.  Trajectory Reconstruction for Self-Localization and Map Building , 2002, ICRA.

[13]  Ben J. A. Kröse,et al.  Towards global consistent pose estimation from images , 2002, IROS.

[14]  William H. Press,et al.  Numerical recipes in C , 2002 .

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

[16]  Olivier D. Faugeras,et al.  The fundamental matrix: Theory, algorithms, and stability analysis , 2004, International Journal of Computer Vision.

[17]  Roland Siegwart,et al.  Introduction to Autonomous Mobile Robots , 2004 .

[18]  Paolo Valigi,et al.  Ball Detection and Predictive Ball Following Based on a Stereoscopic Vision System , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[19]  Davide Scaramuzza,et al.  Sparse codes for natural images , 2005 .

[20]  Roland Siegwart,et al.  Precise Bearing Angle Measurement Based on Omnidirectional Conic Sensor and Defocusing , 2005 .