1-point affine RANSAC for scene image matching in appearance-based localization

This work aims to improve the matching correctness between scene images, which is the key issue in appearance-based localization tasks. Although existing approaches perform well when the test and map/model images are taken at near places, recognizing far-departed images is still challenging because of the deformed and missing features. In this paper, we propose to predict the type of feature deformation and select the robust features when the camera motion is limited to certain directions. The proposed approach can emphasize the importance of truly robust features in localization tasks. The experimental results shows that the proposed approaches outperforms the state-of-art matching algorithms, especially in cases of matching distant images.

[1]  James J. Little,et al.  Vision-based global localization and mapping for mobile robots , 2005, IEEE Transactions on Robotics.

[2]  Vincent Lepetit,et al.  BRIEF: Binary Robust Independent Elementary Features , 2010, ECCV.

[3]  Tom Drummond,et al.  Faster and Better: A Machine Learning Approach to Corner Detection , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Jean Ponce,et al.  A tensor-based algorithm for high-order graph matching , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[5]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[6]  Andrew Zisserman,et al.  Video Google: a text retrieval approach to object matching in videos , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[7]  Davide Scaramuzza,et al.  Performance evaluation of 1‐point‐RANSAC visual odometry , 2011, J. Field Robotics.

[8]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[9]  Gordon Wyeth,et al.  OpenFABMAP: An open source toolbox for appearance-based loop closure detection , 2012, 2012 IEEE International Conference on Robotics and Automation.

[10]  W. Marsden.  I and J , 2012 .

[11]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[12]  Davide Scaramuzza,et al.  1-Point-RANSAC Structure from Motion for Vehicle-Mounted Cameras by Exploiting Non-holonomic Constraints , 2011, International Journal of Computer Vision.

[13]  Richard Szeliski,et al.  Building Rome in a day , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[15]  Gary R. Bradski,et al.  ORB: An efficient alternative to SIFT or SURF , 2011, 2011 International Conference on Computer Vision.

[16]  Cordelia Schmid,et al.  Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[17]  Yihong Gong,et al.  Linear spatial pyramid matching using sparse coding for image classification , 2009, CVPR.

[18]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[19]  Paul Newman,et al.  FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance , 2008, Int. J. Robotics Res..