Mobile robot localization with gyroscope and constrained Kalman filter
Rent the article at a discountRent now
* Final gross prices may vary according to local VAT.Get Access
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.
- Lee, Y.-J., Yim, B.-D., Song, J.-B. (2009) Mobile robot localization based on effective combination of vision and range sensors. Int’l J. of Control, Automation, and Systems 7: pp. 97-104 CrossRef
- EPSON Toyocom Corp., XV-3500CB data sheet — Angular rate sensor [Online], Available: http://www.epsontoyocom.co.jp/english/product/Sensor/XV-3500CB/XV-3500CB.html
- Analog Devices, Inc., ADXRS150 — Angular rate sensor [Online], Available: http://www.analog.com/en/prod/0,2877,ADXRS150,00.html
- Barshan, B., Durrant-Whyte, H. F. (1995) Inertial navigation systems for mobile robots. IEEE Trans. on Robotics and Automation 11: pp. 328-342 CrossRef
- K. Komoriya and E. Oyama, “Position estimation of a mobile robot using optical fiber gyroscope (OFG),” Proc. of IEEE/RSJ Int’l Conf. on Intelligent Robots and Systems, Munich, Germany, pp. 143–149, Sept. 1994.
- S. I. Roumeliotis, G. S. Sukhatme, and G. A. Bekey, “Circumventing dynamic modeling: evaluation of the error-state Kalman filter applied to mobile robot localization,” Proc. of IEEE Int’l Conf. on Robotics and Automation, Detroit, pp. 1656–1663, May 1999.
- Chung, H., Ojeda, L., Borenstein, J. (2001) Accurate mobile robot dead-reckoning with a precision-calibrated fiber-optic gyroscope. IEEE Trans. on Robotics and Automation 17: pp. 80-84 CrossRef
- N. Ma, M. Bouchard, and R. A. Goubran, “Dual perceptually constrained unscented Kalman filter for enhancing speech degraded by colored noise,” Proc. of Int’l Conf. on Signal Processing, 2004.
- Li, W., Leung, H. (2003) Constrained unscented Kalman filter based fusion of GPS/INS/digital map for vehicle localization. Proc. of IEEE Intelligent Transportation Systems 2: pp. 1362-1367
- Geeter, J., Brussel, H., Schutter, J., Decreton, M. (1997) A smoothly constrained Kalman filter. IEEE Trans. on Pattern Analysis and Machine Intelligence 19: pp. 1171-1177 CrossRef
- J. Vaganay, M. J. Aldon, and A. Fourinier, “Mobile robot attitude estimation by fusion of inertial data,” Proc. of IEEE Int’l Conf. on Robotics and Automation, Atlanta, GA, pp. 277–282, May 1993.
- S. G. Mohinder and P. A. Angus, Kalman Filtering: Theory and Practice Using Matlab, 2nd ed., John Wiley & Sons, pp. 43–44, 275–281, 2001.
- B. Southall, B. F. Buxton, and J. A. Marchant, “Controllability and observability: tools for Kalman filter design,” Proc. of the Ninth British Machine Vision Conference, John N. Carter & Mark S. Nixon (eds), pp. 164–173, 1998.
- Azizi, F., Houshangi, N. (2004) Mobile robot position determination using data from gyro and odometry. Proc. of Canadian Conf. on Electrical and Computer Engineering 2: pp. 719-722
- Simon, D., Chia, T. (2002) Kalman filtering with state equality constraints. IEEE Trans. on Aerospace and Electronic Systems 39: pp. 128-136 CrossRef
- D. Simon and D. L. Simon, “Aircraft turbofan engine health estimation using constrained Kalman filtering,” ASME Turbo Expo 2003, Atlanta, GA, GT2003-38584, June 2003.
- Maa, C. Y., Shanblatt, M. A. (1992) A two-phase optimization neural network. IEEE Trans. on Neural Networks 3: pp. 1003-1009 CrossRef
- Kim, J.-H., Myung, H. (1997) Evolutionary programming techniques for constrained optimization problems. IEEE Trans. on Evolutionary Computation 1: pp. 129-140 CrossRef
- H. Myung, H.-K. Lee, K. Choi, S. W. Bang, and S. Kimg, “Constrained Kalman filter for mobile robot localization with gyroscope,” Proc. of IEEE/RSJ Int’l Conf. on Intelligent Robots and Systems, pp. 442–447, Beijing, China, Oct. 2006.
- Rudolph, A. (2003) Quantification and estimation of differential odometry errors in mobile robotics with redundant sensor information. The Int’l Journal of Robotics Research 22: pp. 117-128 CrossRef
- 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