Least-Squares Estimation of Transformation Parameters Between Two Point Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust motion and correspondence of noisy 3-D point sets with missing data
Pattern Recognition Letters
Determining Correspondences and Rigid Motion of 3-D Point Sets with Missing Data
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Adaptive Tracking Control for Robots with Unknown Kinematic and Dynamic Properties
International Journal of Robotics Research
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Controlling a tendon-driven robot like the humanoid Ecce is a difficult task, even more so when its kinematics and its pose are not known precisely. In this paper, we present a visual motion capture system to allow both real-time measurements of robot joint angles and model estimation of its kinematics. Unlike other humanoid robots, Ecce (see Fig. 1A) is completely molded by hand and its joints are not equipped with angle sensors. This anthropomimetic robot design [5] demands for both (i) real-time measurement of joint angles and (ii) model estimation of its kinematics. The underlying principle of this work is that all kinematic model parameters can be derived from visual motion data. Joint angle data finally lay the foundation for physics-based simulation and control of this novel musculoskeletal robot.