Name
Title | ||
|---|---|---|
On-line estimation of inertial parameters using a recursive total least-squares approach |
Abstract | ||
|---|---|---|
The estimation of the ten inertial parameters of rigid loads, which are attached to manipulators, may benefit several robotics applications, e.g.: force control, object recognition, and pose estimation. These applications require sufficiently accurate, robust, and fast estimation of the inertial parameters. Existing approaches, however, do not allow for robust on-line estimation, since they use standard batch least-squares techniques, which ignore noise in the data matrix. The proposed approach, however, estimates the inertial parameters on-line and very fast (approx. 1.5s), while explicitly considering noise in the data matrix by a total least-squares approach. Apart from estimation equations and estimation approaches, the design of estimation trajectories is addressed in this paper. The performance of the proposed estimation approach is compared with the recursive ordinary least-squares (RLS) and the recursive instrumental variables (RIV) method. Experimental results clearly recommend the proposed recursive total least-squares approach (RTLS). |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/IROS.2008.4650672 | Nice |
Keywords | Field | DocType |
least squares approximations,manipulators,recursive estimation,estimation equations,inertial parameters,manipulators,online estimation,recursive total least-squares approach | Inertial frame of reference,Computer science,Control theory,Instrumental variable,Recursive Bayesian estimation,Pose,Artificial intelligence,Total least squares,Trajectory,Recursion,Robotics | Conference |
ISBN | Citations | PageRank |
978-1-4244-2057-5 | 5 | 0.62 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel Kubus | 1 | 48 | 9.02 |
| Torsten Kröger | 2 | 196 | 27.13 |
| Friedrich M. Wahl | 3 | 794 | 186.93 |