Structured light range sensors (SLRS) like the Microsoft Kinect have electronic rolling shutters (ERS). The output of such a sensor while in motion is subject to significant motion blur (MB) and rolling shutter (RS) distortion. Most robotic literature still does not explicitly model this distortion, resulting in inaccurate camera motion estimation. In RGBD cameras, we show via experimentation that the distortion undergone by depth images is different from that of color images and provide a mathematical model for it. We propose an algorithm that rectifies for these RS and MB distortions. To assess the performance of the algorithm we conduct an extensive set of experiments for each step of the pipeline. We assess the performance of our algorithm by comparing the performance of the rectified images on scene-flow and camera pose estimation, and show that with our proposed rectification, the performance improvement is significant.
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