Pose Estimation of Automatic Battery-Replacement System Based on ORB and Improved Keypoints Matching Method

This paper presents an improved Oriented Feature from Accelerated Segment Test (FAST) and Rotated BRIEF (ORB) keypoints matching method for pose estimation of automatic battery-replacement systems. The key issue of the system is how to precisely estimate the pose of the camera in respect to the battery. In our system, the pose-estimation hardware module is mounted onto the robot manipulator, composed of double high brightness LED light source, one monocular camera, and two laser rangefinders. The camera is utilized to take an image of the battery, the laser rangefinders on both sides of the camera are utilized to detect the real-time distance between the battery and the pose-estimation system. The estimation result is significantly influenced by the matching result of the keypoints detected by the ORB technique. The modified matching procedure, based on spatial consistency nearest hamming distance searching method, is used to determine the correct correspondences. Meanwhile, the iterative reprojection error minimization algorithm is utilized to discard incorrect correspondences. Verified by the experiments, the results reveal that this method is highly reliable and able to achieve the required positioning accuracy. The positioning error is lower than 1 mm.

[1]  Jean-Yves Bouguet,et al.  Camera calibration toolbox for matlab , 2001 .

[2]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[3]  Andrew Zisserman,et al.  Image Classification using Random Forests and Ferns , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[4]  David M. Mount,et al.  Efficient algorithms for robust feature matching , 1999, Pattern Recognit..

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

[6]  Gregory D. Hager,et al.  Fast and Globally Convergent Pose Estimation from Video Images , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Zhengyou Zhang,et al.  A Flexible New Technique for Camera Calibration , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Cordelia Schmid,et al.  Bandit Algorithms for Tree Search , 2007, UAI.

[9]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

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

[11]  Fadi Dornaika,et al.  Object Pose: The Link between Weak Perspective, Paraperspective, and Full Perspective , 1997, International Journal of Computer Vision.

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

[13]  Paul L. Rosin.  Measuring Corner Properties , 1999, Comput. Vis. Image Underst..

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

[15]  Jeffrey A. Jalkio,et al.  Three dimensional inspection using multistripe structured light , 1985 .

[16]  V. Lepetit,et al.  EPnP: An Accurate O(n) Solution to the PnP Problem , 2009, International Journal of Computer Vision.

[17]  Vincent Lepetit,et al.  Accurate Non-Iterative O(n) Solution to the PnP Problem , 2007, 2007 IEEE 11th International Conference on Computer Vision.

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

[19]  Leonidas J. Guibas,et al.  Frustum PointNets for 3D Object Detection from RGB-D Data , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[20]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[21]  Pietro Perona,et al.  Fast Feature Pyramids for Object Detection , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  Matthew S. Hölzel,et al.  A comparison of prefilters in ORB-based object detection , 2017, Pattern Recognit. Lett..

[23]  Ming-Chuan Leu,et al.  Design of optical triangulation devices , 1989 .

[24]  Yaser Sheikh,et al.  OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[25]  David G. Lowe,et al.  Scalable Nearest Neighbor Algorithms for High Dimensional Data , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[27]  Kostas Daniilidis,et al.  Linear Pose Estimation from Points or Lines , 2002, IEEE Trans. Pattern Anal. Mach. Intell..