Name
Abstract | ||
|---|---|---|
This paper introduces a novel batch optimization based calibration framework for legged robots. Given a non-degenerate calibration dataset and considering the stochastic models of the sensors, the task is formulated as a maximum likelihood problem. In order to facilitate the derivation of consistent measurement equations, the trajectory of the robot and other auxiliary variables are included into the optimization problem. This formulation can be transformed into a nonlinear least squares problem which can be readily solved. Applied to our legged robot StarlETH, the framework estimates kinematic parameters (segment lengths, body dimensions, angular offsets), accelerometer and gyroscope biases, as well as full inter-sensor calibrations. The generic structure easily allows the inclusion of additional sensor modalities. Based on datasets obtained on the real robot the consistency and performance of the presented approach are successfully evaluated. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICRA.2013.6630924 | 2013 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA) |
Keywords | Field | DocType |
maximum likelihood estimation,accelerometer,calibration,sensors,kinematics,stochastic processes,robot kinematics | Robot control,Kinematics,Robot calibration,Control theory,Legged robot,Robot kinematics,Control engineering,Robot,Optimization problem,Mobile robot,Mathematics | Conference |
Volume | Issue | ISSN |
2013 | 1 | 1050-4729 |
Citations | PageRank | References |
1 | 0.38 | 11 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael Blösch | 1 | 427 | 31.24 |
| Marco Hutter | 2 | 460 | 58.00 |
| Christian Gehring | 3 | 180 | 13.79 |
| Mark A. Höpflinger | 4 | 8 | 2.13 |
| Roland Siegwart | 5 | 7640 | 551.49 |