Least-Squares Fitting of Two 3-D Point Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Robotics Research
Motor Algebra for 3D Kinematics: The Case of the Hand-Eye Calibration
Journal of Mathematical Imaging and Vision
The geometry and algebra of kinematics
Geometric computing with Clifford algebras
Three D-Dynamic Scene Analysis: A Stereo Based Approach
Three D-Dynamic Scene Analysis: A Stereo Based Approach
The Dual Quaternion Approach to Hand-Eye Calibration
ICPR '96 Proceedings of the 1996 International Conference on Pattern Recognition (ICPR '96) Volume I - Volume 7270
Projective reconstruction of all visual primitives
Pattern Recognition
| Hi-index | 0.00 |
In this paper the motor algebra for linearizing the 3D Euclidean motion of lines is used as the oretical basis for the development of a novel extended Kalman filter called the motor extended Kalman filter (MEKF). Due to its nature the MEKF can be used as online approach as opposed to batch SVD methods. The MEKF does not encounter singularities when computing the Kalman gain and it can estimate simultaneously the translation and rotation transformations. Many algorithms in the literature compute the translation and rotation transformations separately. The experimental part demonstrates that the motor extended Kalman filter is an useful approach for estimation of dynamic motion problems. We compare the MEKF with an analytical method using simulated data. We present also an application using real images of a visual guided robot manipulator; the aim of this experiment is to demonstrate how we can use the online MEKF algorithm. After the system has been calibrated, the MEKF estimates accurately the relative position of the end-effector and a 3D reference line. We believe that future vision systems being reliably calibrated will certainly make great use of the MEKF algorithm.