Fast and accurate humanoid robot navigation guided by stereovision

Stair-climbing and moving object grasping both require high precision information feedback of the feature coordinate. This manuscript mainly describes how to process the information acquired from the stereovision system in a fast and accurate way and how to use the data to compensate the progressive error caused by the humanoid robot. From camera calibration to image processing to stereo match, every step in the whole vision information processing process plays an important role in getting high precision. Less time consuming can make the robot adapt to the changes of environment quickly, especially in dynamic environment. In this paper, two humanoid robot common tasks are chosen as experiments to verify the effectiveness of the proposed methods. Stair climbing experiment verifies the high precision in long distances and also by using the image information to compensate the progressive error caused by long distance walking and moving object grasping experiment verifies the less time consuming.

[1]  Paul R. Cohen,et al.  Camera Calibration with Distortion Models and Accuracy Evaluation , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  R. Y. Tsai,et al.  An Efficient and Accurate Camera Calibration Technique for 3D Machine Vision , 1986, CVPR 1986.

[3]  Lu Lu,et al.  High precision camera calibration in vision measurement , 2007 .

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

[5]  Zhengyou Zhang,et al.  Flexible camera calibration by viewing a plane from unknown orientations , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[6]  Guangjun Zhang,et al.  A camera calibration method based on iterated extended Kalman filter using planar target , 2006, International Symposium on Instrumentation and Control Technology.

[7]  L. Wang,et al.  Design of Dexterous Arm-Hand for Human-Assisted Manipulation , 2008, ICIRA.

[8]  Sundaram Ganapathy,et al.  Decomposition of transformation matrices for robot vision , 1984, Pattern Recognition Letters.

[9]  Songde Ma,et al.  Implicit and Explicit Camera Calibration: Theory and Experiments , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  G. J. Castro,et al.  An effective camera calibration method , 1998, AMC'98 - Coimbra. 1998 5th International Workshop on Advanced Motion Control. Proceedings (Cat. No.98TH8354).

[11]  Douglas J. Ebelherr The Real Value , 2004 .

[12]  Stergios I. Roumeliotis,et al.  A Kalman Filter-Based Algorithm for IMU-Camera Calibration: Observability Analysis and Performance Evaluation , 2008, IEEE Transactions on Robotics.

[13]  Stergios I. Roumeliotis,et al.  1|A Kalman filter-based algorithm for IMU-camera calibration , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[14]  Roger Y. Tsai,et al.  A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses , 1987, IEEE J. Robotics Autom..

[15]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[16]  D. C. Brown,et al.  Lens distortion for close-range photogrammetry , 1986 .

[17]  M. Hebert,et al.  The Representation, Recognition, and Locating of 3-D Objects , 1986 .