A Rolling Shutter Compliant Method for Localisation and Reconstruction

Nowadays Rolling shutter CMOS cameras are embedded on a lot of devices. This type of cameras does not have its retina exposed simultaneously but line by line. The resulting distortions affect structure from motion methods developed for global shutter, like CCD cameras. The bundle adjustment method presented in this paper deals with rolling shutter cameras. We use a projection model which considers pose and velocity and need 6 more parameters for one view in comparison to the global shutter model. We propose a simplified model which only considers distortions due to rotational speed. We compare it to the global shutter model and the full rolling shutter one. The model does not need any condition on the inter-frame motion so it can be applied to fully independent views, even with global shutter images equivalent to a null velocity. Results with both synthetic and real images shows that the simplified model can be considered as a good compromise between a correct geometrical modelling of rolling shutter effects and the reduction of the number of extra parameters. Keywords

[1]  Anastasios I. Mourikis,et al.  Real-time motion tracking on a cellphone using inertial sensing and a rolling-shutter camera , 2013, 2013 IEEE International Conference on Robotics and Automation.

[2]  Tim D. Barfoot,et al.  Towards relative continuous-time SLAM , 2013, 2013 IEEE International Conference on Robotics and Automation.

[3]  Ludovic Magerand,et al.  Global Optimization of Object Pose and Motion from a Single Rolling Shutter Image with Automatic 2D-3D Matching , 2012, ECCV.

[4]  Michel Dhome,et al.  Generic and real-time structure from motion using local bundle adjustment , 2009, Image Vis. Comput..

[5]  Richard Szeliski,et al.  Removing rolling shutter wobble , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[6]  S. Shankar Sastry,et al.  Geometric Models of Rolling-Shutter Cameras , 2005, ArXiv.

[7]  Michael Felsberg,et al.  Rolling shutter bundle adjustment , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Michel Dhome,et al.  Monocular Vision for Mobile Robot Localization and Autonomous Navigation , 2007, International Journal of Computer Vision.

[9]  Homer H. Chen,et al.  Analysis and Compensation of Rolling Shutter Effect , 2008, IEEE Transactions on Image Processing.

[10]  Philippe Martinet,et al.  Simultaneous Object Pose and Velocity Computation Using a Single View from a Rolling Shutter Camera , 2006, ECCV.

[11]  Berthold K. P. Horn,et al.  Closed-form solution of absolute orientation using unit quaternions , 1987 .

[12]  Per-Erik Forssén,et al.  Efficient Video Rectification and Stabilisation for Cell-Phones , 2012, International Journal of Computer Vision.

[13]  Andrew I. Comport,et al.  A Unified Rolling Shutter and Motion Blur Model for 3D Visual Registration , 2013, 2013 IEEE International Conference on Computer Vision.

[14]  Nicolas Andreff,et al.  Kinematics from Lines in a Single Rolling Shutter Image , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[15]  Derek Bradley,et al.  Synchronization and rolling shutter compensation for consumer video camera arrays , 2009, 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[16]  Marc Pollefeys,et al.  Rolling Shutter Stereo , 2013, 2013 IEEE International Conference on Computer Vision.

[17]  Michael Felsberg,et al.  Structure and motion estimation from rolling shutter video , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

[18]  François Berry,et al.  Structure and kinematics triangulation with a rolling shutter stereo rig , 2009, 2009 IEEE 12th International Conference on Computer Vision.