Odometry provides fundamental pose estimates for wheeled vehicles. For accurate and reliable pose estimation, systematic and nonsystematic errors of odometry should be reduced. In this paper, we focus on systematic error sources of a car-like mobile robot (CLMR) and we suggest a novel calibration method. Kinematic parameters of the CLMR can be successfully calibrated by only a couple of test driving. After reducing deterministic errors by calibration, odometry accuracy can be further improved by redundant odometry fusion with the extended Kalman filter (EKF). Odometry fusion reduces nonsystematic or stochastic errors. Experimental verifications are carried out using a radio-controlled miniature car.
|Number of pages||14|
|Publication status||Published - 2010 Aug|
Bibliographical noteFunding Information:
This work was supported in part by the MKE under the Human Resources Development Program for Convergence Robot Specialists. This work was also supported by Mid-career Researcher Program through the NRF grant funded by the MEST. This work was also supported by the ITRC support program. This work was also supported by the project “The Development of Autonomous Navigation Systems for a Robot Vehicle in Urban Environment” at the KIST. This work was also supported by Korea University during the sabbatical year of the second author.
- Mobile robot
- Pose recognition
ASJC Scopus subject areas
- Mechanical Engineering
- Computer Science Applications
- Electrical and Electronic Engineering