Scale-Space Features in 1D Omnidirectional Images

We define a family of interest operators for extracting features from one-dimensional omnidirectional images, and explore the utility of such features for navigation and localization of a mobile robot equipped with an omnidirec- tional camera. A 1D circular image, formed by averaging the scanlines of a cylin- drical panorama, provides a compact representation of the robot's surroundings. Feature detection proceeds by applying local interest operators in the scale space of the image. The work is inspired by the recent success of similar operators de- veloped for 2D images. The advantages of using features in omnidirectional 1D images are fast processing times and low storage requirements, which allows a dense sampling of views. We present experimental results on real images that demonstrate that our features are insensitive to noise, illumination variations, and changes in camera orientation. We also demonstrate that most features remain stable over changes in viewpoint and in the presence of some occlusion, thus allowing reliable tracking of features through sequences of frames.

[1]  Shree K. Nayar,et al.  360/spl times/360 mosaics , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[2]  James J. Little,et al.  Global localization using distinctive visual features , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[3]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[4]  C. J. Taylor,et al.  Structure and motion in two dimensions from multiple images: a least squares approach , 1991, Proceedings of the IEEE Workshop on Visual Motion.

[5]  Demetri Terzopoulos,et al.  Signal matching through scale space , 1986, International Journal of Computer Vision.

[6]  Saburo Tsuji,et al.  Panoramic representation for route recognition by a mobile robot , 1992, International Journal of Computer Vision.

[7]  Robert C. Bolles,et al.  Epipolar-plane image analysis: a technique for analyzing motion sequences , 1987 .

[8]  Hugh F. Durrant-Whyte,et al.  Simultaneous Mapping and Localization with Sparse Extended Information Filters: Theory and Initial Results , 2004, WAFR.

[9]  Shree K. Nayar,et al.  360 x 360 Mosaics , 2000, Computer Vision and Pattern Recognition.

[10]  Tony Lindeberg,et al.  Scale-Space for Discrete Signals , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Amy J. Briggs,et al.  Real-time recognition of self-similar landmarks , 2001, Image Vis. Comput..

[12]  Yasushi Yagi,et al.  Map-based navigation for a mobile robot with omnidirectional image sensor COPIS , 1995, IEEE Trans. Robotics Autom..

[13]  Tony Lindeberg,et al.  Feature Detection with Automatic Scale Selection , 1998, International Journal of Computer Vision.

[14]  Cordelia Schmid,et al.  A Performance Evaluation of Local Descriptors , 2005, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Tony Lindeberg,et al.  Detecting salient blob-like image structures and their scales with a scale-space primal sketch: A method for focus-of-attention , 1993, International Journal of Computer Vision.

[16]  Andrew P. Witkin,et al.  Scale-Space Filtering , 1983, IJCAI.

[17]  Yasushi Yagi,et al.  Real-time omnidirectional image sensor (COPIS) for vision-guided navigation , 1994, IEEE Trans. Robotics Autom..

[18]  James J. Little,et al.  Mobile Robot Localization and Mapping with Uncertainty using Scale-Invariant Visual Landmarks , 2002, Int. J. Robotics Res..

[19]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[20]  Cordelia Schmid,et al.  An Affine Invariant Interest Point Detector , 2002, ECCV.

[21]  Matthew A. Brown,et al.  Invariant Features from Interest Point Groups , 2002, BMVC.

[22]  Hugh F. Durrant-Whyte,et al.  A solution to the simultaneous localization and map building (SLAM) problem , 2001, IEEE Trans. Robotics Autom..

[23]  James L. Crowley,et al.  A Representation for Shape Based on Peaks and Ridges in the Difference of Low-Pass Transform , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[25]  Václav Hlaváč,et al.  MOTION ESTIMATION USING CENTRAL PANORAMIC CAMERAS , 1998 .

[26]  Xueyin Lin,et al.  Panoramic EPI generation and analysis of video from a moving platform with vibration , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[27]  Masayuki Inaba,et al.  Visual navigation using omnidirectional view sequence , 1999, Proceedings 1999 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human and Environment Friendly Robots with High Intelligence and Emotional Quotients (Cat. No.99CH36289).

[28]  Amy J. Briggs,et al.  Reliable Mobile Robot Navigation From Unreliable Visual Cues , 2000 .

[29]  C. Schmid,et al.  Indexing based on scale invariant interest points , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.