Research on binocular vision absolute localization method for indoor robots based on natural landmarks

A method is put forward to extract feature points in the indoor ceiling corner as natural landmarks by using binocular visual system and to go further to achieve absolute localization for robot. Firstly, this method puts forward a kind of double threshold Features from Accelerated Segment Test (FAST) - Scale Invariant Feature Transform (SIFT) matching algorithm, in which FAST algorithm is applied to extract the feature points in the ceiling corner to reduce the number of feature points effectively. For each feature point, SIFT descriptor and matching algorithm is remained so that the advantages of the SIFT algorithm is inherited. And then the reasonable sampling rules are introduced to create natural landmark database, the SIFT descriptors of feature points in the ceiling corner and their 3-D world coordinates are saved into natural landmark database in offline mode, When robot walks, the feature points are identified by matching left image with all SIFT descriptors in database and then their world coordinates are obtained. In the meantime, the distances between the feature points and robot are measured by matching left image with the right one. Finally, the world coordinates of the robot are computed according to the principle of triangular localization. To demonstrate the effectiveness of a series of method proposed in this paper, we do the corresponding physical experiments. Experimental results show that indoor robot absolute localization method proposed in this paper has a good performance in real-time, reliability and accuracy.

[1]  M. Arif,et al.  Measurement and Correction of Systematic Odometry Errors Caused by Kinematics Imperfections in Mobile Robots , 2006, 2006 SICE-ICASE International Joint Conference.

[2]  Zhang Ling-tao.  Self-localization of mobile robots based on artificial landmarks and stereo vision , 2010 .

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

[4]  Tom Duckett,et al.  3D modeling of indoor environments by a mobile robot with a laser scanner and panoramic camera , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

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

[6]  Wan Kyun Chung,et al.  Terminal iterative learning control for calibrating systematic odometry errors in mobile robots , 2010, 2010 IEEE/ASME International Conference on Advanced Intelligent Mechatronics.

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

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

[9]  Xiang Zhi-yu Liu Ji-lin Chen Ming-ya.  Assistance localization method for mobile robot based on monocular natural visual landmarks , 2014 .

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

[11]  Jae-Bok Song,et al.  Monocular Vision-Based SLAM in Indoor Environment Using Corner, Lamp, and Door Features From Upward-Looking Camera , 2011, IEEE Transactions on Industrial Electronics.