Name
Abstract | ||
|---|---|---|
Mobile robot platforms and sensor mountings are susceptible to accidental contact with undetected objects and to degradation of sensor alignment after extended periods of mechanical operation. For correct sensor data interpretation and to accomplish sensor fusion, this has to be taken into account. Most sensor calibration techniques are computationally expensive or cannot be performed unattended, and therefore are not well suited for autonomous robots. However, by employing additional knowledge about the robot's construction (and sources of error), sufficient accuracy can be maintained by using a simpler adjustment procedure. This paper contributes an enhanced version of the preprocessing algorithm presented by Iocchi (1999) for automated computation of camera radial distortion with fewer demands on the image, as well as a method for self-controlled sensor adjustment by improving the matching of such preprocessed multisensor data. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1109/IRDS.2002.1041496 | Intelligent Robots and Systems, 2002. IEEE/RSJ International Conference |
Keywords | Field | DocType |
calibration,image matching,mobile robots,robot kinematics,robot vision,sensor fusion,adjustment procedure,automated computation,camera radial distortion,controlled sensor adjustment,kinematic deviation compensation,mobile robot,mobile robot platforms,preprocessed multisensor data matching,preprocessing algorithm,self-controlled sensor/platform adjustment,sensor alignment degradation,sensor calibration techniques,sensor data interpretation,sensor fusion,sensor kinematics,sensor mountings,undetected object contact | Computer vision,Robot calibration,Soft sensor,Computer science,Visual sensor network,Robot kinematics,Sensor fusion,Control engineering,Artificial intelligence,Mobile robot navigation,Robot,Mobile robot | Conference |
Volume | ISBN | Citations |
1 | 0-7803-7398-7 | 0 |
PageRank | References | Authors |
0.34 | 3 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Johannes Bitterling | 1 | 0 | 0.34 |
| Mertsching, B.M.C. | 2 | 0 | 0.34 |