ICRA'09 Paper Abstract


Paper FrB10.3

Pretto, Alberto (University of Padua), Menegatti, Emanuele (The University of Padua), Bennewitz, Maren (University of Freiburg), Burgard, Wolfram (University of Freiburg), Pagello, Enrico (University of Padua)

A Visual Odometry Framework Robust to Motion Blur

Scheduled for presentation during the Regular Sessions "Visual Navigation - I" (FrB10), Friday, May 15, 2009, 11:10−11:30, Room: 502

2009 IEEE International Conference on Robotics and Automation, May 12 - 17, 2009, Kobe, Japan

This information is tentative and subject to change. Compiled on January 24, 2022

Keywords Visual Navigation, SLAM, Humanoid Robots


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 non-uniform 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 reliably extract and match features and that it is furthermore able to generate a correct visual odometry, even in presence of strong motion blur effects and without the aid of any inertial measurement sensor.



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