Camera calibration with known rotation

We address the problem of using external rotation information with uncalibrated video sequences. The main problem addressed is, what is the benefit of the orientation information for camera calibration? It is shown that in case of a rotating camera the camera calibration problem is linear even in the case that all intrinsic parameters vary. For arbitrarily moving cameras the calibration problem is also linear but underdetermined for the general case of varying all intrinsic parameters. However, if certain constraints are applied to the intrinsic parameters the camera calibration can be computed linearly. It is analyzed which constraints are needed for camera calibration of freely moving cameras. Furthermore we address the problem of aligning the camera data with the rotation sensor data in time. We give an approach to align these data in case of a rotating camera.

[1]  Roger Y. Tsai,et al.  A new technique for fully autonomous and efficient 3D robotics hand/eye calibration , 1988, IEEE Trans. Robotics Autom..

[2]  Anders Heyden,et al.  Euclidean reconstruction from image sequences with varying and unknown focal length and principal point , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[3]  Reinhard Koch,et al.  Self-calibration and metric reconstruction in spite of varying and unknown internal camera parameters , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[4]  W. Randolph Franklin.  Efficient Iterated Rotation of an Object , 1983, IEEE Transactions on Computers.

[5]  M. Hebert,et al.  The Representation, Recognition, and Locating of 3-D Objects , 1986 .

[6]  Richard I. Hartley.  Self-Calibration from Multiple Views with a Rotating Camera , 1994, ECCV.

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

[8]  C. Pearson,et al.  Handbook of Applied Mathematics , 1975 .

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

[10]  Carsten Rother,et al.  Linear Multi View Reconstruction and Camera Recovery Using a Reference Plane , 2002, International Journal of Computer Vision.

[11]  Bill Triggs,et al.  Autocalibration and the absolute quadric , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[12]  Olivier D. Faugeras,et al.  A theory of self-calibration of a moving camera , 1992, International Journal of Computer Vision.

[13]  Ian D. Reid,et al.  Self-Calibration of Rotating and Zooming Cameras , 2002, International Journal of Computer Vision.

[14]  Reinhard Koch,et al.  Robust Camera Calibration from Images and Rotation Data , 2003, DAGM-Symposium.

[15]  Wolfgang Spohn,et al.  The Representation of , 1986 .

[16]  A. Heyden,et al.  Euclidean reconstruction from constant intrinsic parameters , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[17]  Yaron Caspi,et al.  Alignment of non-overlapping sequences , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[18]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

[19]  Gideon P. Stein.  Accurate internal camera calibration using rotation, with analysis of sources of error , 1995, Proceedings of IEEE International Conference on Computer Vision.

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

[21]  Heung-Yeung Shum,et al.  Panoramic Image Mosaics , 1998 .

[22]  Eric Foxlin,et al.  Circular data matrix fiducial system and robust image processing for a wearable vision-inertial self-tracker , 2002, Proceedings. International Symposium on Mixed and Augmented Reality.

[23]  Michael Brady,et al.  Self-calibration of the intrinsic parameters of cameras for active vision systems , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.