Kinematics from Lines in a Single Rolling Shutter Image

Recent work shows that recovering pose and velocity from a single view of a moving rigid object is possible with a rolling shutter camera, based on feature point correspondences. We extend this method to line correspondences. Owing to the combined effect of rolling shutter and object motion, straight lines are distorted to curves as they get imaged with a rolling shutter camera. Lines thus capture more information than points, which is not the case with standard projection models for which both points and lines give two constraints. We extend the standard line reprojection error, and propose a nonlinear method for retrieving a solution to the pose and velocity computation problem. A careful inspection of the design matrix in the normal equations reveals that it is highly sparse and patterned. We propose a blockwise solution procedure based on bundle-adjustment-like sparse inversion. This makes nonlinear optimization fast and numerically stable. The method is validated using real data.