Relative Pose Estimation for Stereo Rolling Shutter Cameras
This work addresses relative pose estimation for stereo rolling shutter cameras, which is an incremental improvement for computer vision applications like 3D reconstruction.
The paper tackles the problem of estimating 6 DoF relative pose from stereo rolling shutter camera frames by proposing a linear algorithm that uses 9 correspondence pairs and constant velocity motion assumptions, with experiments on simulated and synthetic data showing its effectiveness.
In this paper, we present a novel linear algorithm to estimate the 6 DoF relative pose from consecutive frames of stereo rolling shutter (RS) cameras. Our method is derived based on the assumption that stereo cameras undergo motion with constant velocity around the center of the baseline, which needs 9 pairs of correspondences on both left and right consecutive frames. The stereo RS images enable the recovery of depth maps from the semi-global matching (SGM) algorithm. With the estimated camera motion and depth map, we can correct the RS images to get the undistorted images without any scene structure assumption. Experiments on both simulated points and synthetic RS images demonstrate the effectiveness of our algorithm in relative pose estimation.