Motion Deblurring using Coded Exposure for a Wheeled Mobile Robot

Abstract

We present a motion deblurring framework for a wheeled mobile robot. Motion blur is an inevitable problem in a mobile robot, especially side-view cameras severely suffer from motion blur when a mobile robot moves forward. To handle motion blur in a robot, we develop a fast motion deblurring framework using the concept of coded exposure. We estimate a blur kernel by a simple template matching between adjacent frames with a motion prior and a blind deconvolution algorithm with a Gaussian prior is exploited for fast deblurring. Our system is implemented using an off-the-shelf machine vision camera and enables us to achieve high-quality deblurring results with little computation time. We demonstrate the effectiveness of our system to handle motion blur and validate it is useful for many robot applications such as text recognition and visual structure from motion.