On Tracking and Matching in Vision Based Navigation

The paper presents a thorough comparative analysis of the feature tracking and the feature matching approaches applied to the visual navigation. The evaluation was performed on a synthetic dataset with perfect ground truth to assure maximum reliability of results. The presented results include the analysis of both the feature localization accuracy and the computational costs of different methods. Additionally, the distribution of the uncertainty of the features localization was analyzed and parametrized.

[1]  Andrew J. Davison,et al.  A benchmark for RGB-D visual odometry, 3D reconstruction and SLAM , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[2]  Hakil Kim,et al.  INHA: Localization of Mobile Robots Based on Feature Matching with a Single Camera , 2013, 2013 IEEE International Conference on Systems, Man, and Cybernetics.

[3]  Chao Li,et al.  Monocular vision simultaneous localization and mapping using SURF , 2008, 2008 7th World Congress on Intelligent Control and Automation.

[4]  Antti Oulasvirta,et al.  Computer Vision – ECCV 2006 , 2006, Lecture Notes in Computer Science.

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

[6]  Vincent Lepetit,et al.  BRIEF: Computing a Local Binary Descriptor Very Fast , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  J.-Y. Bouguet,et al.  Pyramidal implementation of the lucas kanade feature tracker , 1999 .

[8]  F. Fraundorfer,et al.  Visual Odometry : Part II: Matching, Robustness, Optimization, and Applications , 2012, IEEE Robotics & Automation Magazine.

[9]  Michal R. Nowicki,et al.  Combining photometric and depth data for lightweight and robust visual odometry , 2013, 2013 European Conference on Mobile Robots.

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

[11]  Tom Drummond,et al.  Machine Learning for High-Speed Corner Detection , 2006, ECCV.

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

[13]  Alicja Wakulicz-Deja,et al.  Man-Machine Interactions 3, Proceedings of the 3rd International Conference on Man-Machine Interactions, ICMMI 2013, Brenna, Poland, October 22-25, 2013 , 2014, ICMMI.

[14]  Hauke Strasdat,et al.  Scale Drift-Aware Large Scale Monocular SLAM , 2010, Robotics: Science and Systems.

[15]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[16]  Marek Kraft,et al.  Comparative assessment of point feature detectors in the context of robot navigation , 2013 .

[17]  Adam Schmidt,et al.  Visual Simultaneous Localization and Mapping with Direct Orientation Change Measurements , 2013, ICMMI.

[18]  Carlo Tomasi,et al.  Good features to track , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.