Phase Correlation for Dense Visual Compass From Omnidirectional Camera-Robot Images

In this paper, we present a new omnidirectional visual compass for a camera-robot, based on the phase correlation method in the two-dimensional Fourier domain. The proposed visual compass is accurate, robust to image noise, and frugal in the use of computational resources. Moreover, unlike the majority of existing ego-motion estimators, it does not rely on any geometric image primitive, and it only requires a minimal knowledge of the internal camera parameters. Extensive real-world experiments conducted with a hypercatadioptric camera mounted on the end-effector of a Stäubli manipulator and on a Pioneer robot show the effectiveness of our approach.

[1]  Y. Keller,et al.  Volume Registration Using the 3-D Pseudopolar Fourier Transform , 2006, IEEE Transactions on Signal Processing.

[2]  Friedrich Fraundorfer,et al.  Visual Odometry Part I: The First 30 Years and Fundamentals , 2022 .

[3]  Emanuele Menegatti,et al.  Image-based memory for robot navigation using properties of omnidirectional images , 2004, Robotics Auton. Syst..

[4]  Hanspeter A. Mallot,et al.  Efficient visual homing based on Fourier transformed panoramic images , 2006, Robotics Auton. Syst..

[5]  Tim Kazik,et al.  Visual odometry based on the Fourier-Mellin transform for a rover using a monocular ground-facing camera , 2011, 2011 IEEE International Conference on Mechatronics.

[6]  Andrew Vardy,et al.  An Orientation Invariant Visual Homing Algorithm , 2012, J. Intell. Robotic Syst..

[7]  Gregory S. Chirikjian,et al.  Pattern Matching as a Correlation on the Discrete Motion Group , 1999, Comput. Vis. Image Underst..

[8]  Roland Chapuis,et al.  Radar Scan Matching SLAM Using the Fourier-Mellin Transform , 2009, FSR.

[9]  I. Du,et al.  Direct Methods , 1998 .

[10]  Houssem-Eddine Benseddik,et al.  Camera rotation estimation using 3D mesh surfaces representation of spherical images , 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[11]  Alessandro Rizzi,et al.  Unsupervised matching of visual landmarks for robotic homing using Fourier-Mellin transform , 2002, Robotics Auton. Syst..

[12]  G. Watson.  Computing Helmert transformations , 2006 .

[13]  Éric Marchand,et al.  ViSP for visual servoing: a generic software platform with a wide class of robot control skills , 2005, IEEE Robotics & Automation Magazine.

[14]  B. Hofmann-Wellenhof,et al.  Introduction to spectral analysis , 1986 .

[15]  C. Morandi,et al.  Registration of Translated and Rotated Images Using Finite Fourier Transforms , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Frédéric Labrosse,et al.  The visual compass: Performance and limitations of an appearance‐based method , 2006, J. Field Robotics.

[17]  Guillaume Caron,et al.  Multiple camera types simultaneous stereo calibration , 2011, 2011 IEEE International Conference on Robotics and Automation.

[18]  Josechu J. Guerrero,et al.  Hypercatadioptric line images for 3D orientation and image rectification , 2012, Robotics Auton. Syst..

[19]  Boaz Porat,et al.  A course in digital signal processing , 1996 .

[20]  Pascal Vasseur,et al.  Rotation estimation and vanishing point extraction by omnidirectional vision in urban environment , 2012, Int. J. Robotics Res..

[21]  Domenico Prattichizzo,et al.  An accurate and robust visual-compass algorithm for robot-mounted omnidirectional cameras , 2012, Robotics Auton. Syst..

[22]  Kostas Daniilidis,et al.  Direct 3D-rotation estimation from spherical images via a generalized shift theorem , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[23]  Wilhelm Burger,et al.  Digital Image Processing - An Algorithmic Introduction using Java , 2008, Texts in Computer Science.

[24]  Javier-Flavio Vigueras,et al.  Phase correlation with sub-pixel accuracy: A comparative study in 1D and 2D , 2015, Comput. Vis. Image Underst..

[25]  Václav Hlavác,et al.  Zero Phase Representation of Panoramic Images for Image Vased Localization , 1999, CAIP.

[26]  Kostas Daniilidis,et al.  Correspondence-free Structure from Motion , 2007, International Journal of Computer Vision.

[27]  João Pedro Barreto,et al.  A unifying geometric representation for central projection systems , 2006, Comput. Vis. Image Underst..

[28]  Éric Marchand,et al.  3D model based tracking for omnidirectional vision: A new spherical approach , 2012, Robotics Auton. Syst..

[29]  Jan Flusser,et al.  Image registration methods: a survey , 2003, Image Vis. Comput..

[30]  G. Chirikjian,et al.  Engineering Applications of Noncommutative Harmonic Analysis: With Emphasis on Rotation and Motion Groups , 2000 .

[31]  Roland Siegwart,et al.  Appearance-Guided Monocular Omnidirectional Visual Odometry for Outdoor Ground Vehicles , 2008, IEEE Transactions on Robotics.

[32]  Yoel Shkolnisky,et al.  The angular difference function and its application to image registration , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.