A minimal solution to the rolling shutter pose estimation problem

Artefacts that are present in images taken from a moving rolling shutter camera degrade the accuracy of absolute pose estimation. To alleviate this problem, we introduce an addition linear velocity in the camera projection matrix to approximate the motion of the rolling shutter camera. In particular, we derive a minimal solution using the Gröbner Basis that solves for the absolute pose as well as the motion of a rolling shutter camera. We show that the minimal problem requires 5-point correspondences and gives up to 8 real solutions. We also show that our formulation can be extended to use more than 5-point correspondences. We use RANSAC to robustly get all the inliers. In the final step, we relax the linear velocity assumption and do a non-linear refinement on the fuli motion, i. e. linear and angular velocities, and pose of the rolling shutter camera with all the inliers. We verify the feasibility and accuracy of our algorithm with both simulated and real-world datasets.

Authors

• Olivier Saurer
• Marc Pollefeys
• Gim Hee Lee

Keywords

• Cameras
• Robustness
• Angular velocity
• Nonlinear distortion
• Polynomials
• Estimation Problem
• Pose Estimation
• Minimal Solution
• Rolling Shutter
• Pose Estimation Problem
• Real-world Datasets
• Linear Velocity
• Real Solution
• Absolute Estimates
• Camera Pose
• Camera Motion
• Inliers
• Photodetector
• Singular Value Decomposition
• Constant Velocity
• Line Scan
• Image Noise
• Corresponding Points
• Optical Flow
• Translational Velocity
• Translation Error
• Rotation Error
• World Frame
• Independent Equations
• Bundle Adjustment
• Homography
• Reprojection Error
• Angular Error
• Motion Blur