A visual odometry framework robust to motion blur

— Motion blur is a severe problem in images grabbed by legged robots and, in particular, by small humanoid robots. Standard feature extraction and tracking approaches typically fail when applied to sequences of images strongly affected by motion blur. In this paper, we propose a new feature detection and tracking scheme that is robust even to nonuniform motion blur. Furthermore, we developed a framework for visual odometry based on features extracted out of and matched in monocular image sequences. To reliably extract and track the features, we estimate the point spread function (PSF) of the motion blur individually for image patches obtained via a clustering technique and only consider highly distinctive features during matching. We present experiments performed on standard datasets corrupted with motion blur and on images taken by a camera mounted on walking small humanoid robots to show the effectiveness of our approach. The experiments demonstrate that our technique is able to re...

• Type: Conference
• Year: 2009
• Where: ICRA
• Authors: Alberto Pretto, Emanuele Menegatti, Maren Bennewitz, Wolfram Burgard, Enrico Pagello
• Keywords: ICRA 2009, Motion Blur, Nonuniform Motion Blur, Robotics, Small Humanoid Robots