Name
Abstract | ||
|---|---|---|
This paper proposes an object recognition and gripping pose estimation approach based on on-tine estimation of the complete set of inertial parameters, i.e. the mass, the coordinates of the center of mass, and the elements of the inertia matrix, of an object gripped by or attached to a manipulator. A multi-sensor fusion approach combining 6D force/torque, 6D acceleration, 3D angular velocity, and joint angle data to estimate these parameters is presented. In order to facilitate practical implementation, approaches to handling force/torque sensor offsets and to compensating the forces/torques caused by the distal mounting plate of the force/torque sensor and the gripper are incorporated. Regarding the joint angle signals, preprocessing steps to derive the angular velocity, linear acceleration and angular acceleration vector w.r.t. the sensor frame are addressed. The estimation of the complete set of inertial parameters employing the recursive instrumental variables (RIV) method is discussed. The extraction of features that are invariant w.r.t. translation and rotation, i.e. the mass and the principal moments of inertia, as well as a recognition approach based on the Kullback-Leibler divergence are presented. Experimental results show very low errors in the estimates of the inertial parameters, good pose estimation accuracy, and the viability of the recognition approach. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/IROS.2007.4399184 | 2007 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS, VOLS 1-9 |
Keywords | Field | DocType |
instrumental variable,torque sensor,sensor fusion,estimation theory,angular acceleration,kullback leibler divergence,inertia matrix,manipulator,feature extraction,linear accelerator,force sensor,moments of inertia,moment of inertia,object recognition,pose estimation,center of mass,parameter estimation,angular velocity | Moment of inertia,Angular velocity,Torque,Computer science,Control theory,Angular acceleration,Pose,Control engineering,Artificial intelligence,Inertial frame of reference,Computer vision,Torque sensor,Acceleration | Conference |
Citations | PageRank | References |
9 | 0.73 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel Kubus | 1 | 48 | 9.02 |
| Torsten Kröger | 2 | 196 | 27.13 |
| Friedrich M. Wahl | 3 | 794 | 186.93 |