Mobile robot localization with gyroscope and constrained Kalman filter
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The odometry information used in mobile robot localization can contain a significant number of errors when robot experiences slippage. To offset the presence of these errors, the use of a low-cost gyroscope in conjunction with Kalman filtering methods has been considered by many researchers. However, results from conventional Kalman filtering methods that use a gyroscope with odometry can unfeasible because the parameters are estimated regardless of the physical constraints of the robot. In this paper, a novel constrained Kalman filtering method is proposed that estimates the parameters under the physical constraints using a general constrained optimization technique. The state observability is improved by additional state variables and the accuracy is also improved through the use of a nonapproximated Kalman filter design. Experimental results show that the proposed method effectively offsets the localization error while yielding feasible parameter estimation.
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- Mobile robot localization with gyroscope and constrained Kalman filter
International Journal of Control, Automation and Systems
Volume 8, Issue 3 , pp 667-676
- Cover Date
- Print ISSN
- Online ISSN
- Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers
- Additional Links
- Kalman filtering
- mobile robot
- Industry Sectors
- Author Affiliations
- 1. Dept. of Civil & Environmental Engineering, KAIST, Guseong-dong, Yuseong-gu, Daejeon, 305-701, Korea
- 2. Samsung Advanced Institute of Technology, Samsung Electronics Co., Ltd., Nongseodong, Giheung-gu, Yongin, Korea
- 3. Robotics Institute, CMU, Pittsburgh, PA, 15213, USA