Hostname: page-component-76fb5796d-5g6vh Total loading time: 0 Render date: 2024-04-26T06:17:23.374Z Has data issue: false hasContentIssue false
  • English
  • Français

An efficient approach to pose tracking based on odometric error modelling for mobile robots

Published online by Cambridge University Press:  01 April 2014

Jingdong Yang ,
Jinghui Yang  and
Zesu Cai
Jingdong Yang*
Affiliation:
School of Optical-Electrical and Computer Engineering, Shanghai Key Lab of Modern Optical System, University of Shanghai for Science and Technology, Shanghai 200093, China
Jinghui Yang
Affiliation:
School of Business Management, Shanghai Second Polytechnic University, Shanghai 201209, China
Zesu Cai
Affiliation:
Department of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
*
*Corresponding author. E-mail: eerfriend@126.com
Get access Rights & Permissions [Opens in a new window]

Summary

Odometric error modelling for mobile robots is the basis of pose tracking. Without bounds the odometric accumulative error decreases localisation precision after long-range movement, which is often not capable of being compensated for in real time. Therefore, an efficient approach to odometric error modelling is proposed in regard to different drive type mobile robots. This method presents a hypothesis that the motion path approximates a circular arc. The approximate functional expressions between the control input of odometry and non-systematic error as well as systematic error derived from odometric error propagation law. Further an efficient algorithm of pose tracking is proposed for mobile robots, which is able to compensate for the non-systematic and systematic error in real time. These experiments denote that the odometric error modelling reduces the accumulative error of odometry efficiently and improves the specific localisation process significantly during autonomous navigation.

Keywords

Extended Kalman filterOdometric error modellingNon-systematic error covariancePose estimateGlobal localisation
Type
Articles
Information
Robotica , Volume 33 , Issue 6 , July 2015 , pp. 1231 - 1249
Copyright
Copyright © Cambridge University Press 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Yang, J.-D., Yang, J.-H. and Hong, B.-R., “An efficient approach to odometric error modeling for mobile robots,” Acta Autom. Sin. 35 (2), 168 (2009).Google Scholar
2. Antonelli, G. and Chiaverini, S., “Linear estimation of the physical odometric parameters for differential-drive mobile robots,” Auton. Robots 23 (1), 59–68 (2007).Google Scholar
3. Antonelli, G. and Chiaverini, S., “Linear Estimation of the Odometric Parameters for Differential-Drive Mobile Robots,” 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, Beijing, PRC (Oct. 2006) pp. 3287–3292.Google Scholar
4. Korodi, A. and Dragomir, T. L., “Correcting Odometry Errors for Mobile Robots Using Image Processing,” Proceedings of the International MultiConference of Engineers and Computer Scientists (IMECS 2010) Vol II, Hong Kong (Mar. 17–19, 2010).Google Scholar
5. Graovac, D., Juriü-Kavelj, S. and Petrović, I., “Mobile robot pose tracking by correlation of laser range finder scans in Hough domain,” IEEE 19th International Workshop on Robotics in Alpe Adria Danube Region RAAD, IEEE, Issue 2 (2010) pp. 273–278 Google Scholar
6. Rusdinar, A., Kim, J. and Kim, S., “Error pose correction of mobile robot for SLAM problem using laser range finder based on particle filter,” System 200, 52–55 (2010).Google Scholar
7. Chong, K. S. and Kleeman, L., “Feature-based mapping in real, large scale environments using an ultrasonic array,” Int. J. Robot. Res. 18 (1), 3–19 (1999).Google Scholar
8. Chong, K. S. and Kleeman, L., “Accurate Odometry and Error Modeling for a Mobile Robot,” International Conference on Robotics and Automation, vol. 4 (1997) pp. 2783–2788.Google Scholar
9. Martinelli, A., “State estimation based on the concept of continuous symmetry and observability analysis: The case of calibration,” IEEE Trans. Robot. - TRob 27 (2), 239–255 (2011).Google Scholar
10. Martinelli, A., “Using the Distribution Theory to Simultaneously Calibrate the Sensor of a Mobile Robot,” Robotics Science and Systems (RSS'09), Seattle, USA (2009).Google Scholar
11. Josep, M., Jose, L.G. and Carlos, A. B., “A closed-form expression for the uncertainty in odometry position estimate of an autonomous vehicle,” IEEE Trans. Robot. 21 (5), 345–401 (2005).Google Scholar
12. Tur, J. M. M., Albores-Borja, C. and Gordillo, J. L., “Erratum to: A closed-form expression for the uncertainty in odometry position estimate of an autonomous vehicle,” IEEE Trans. Robot. 23 (6), 1302 (2007).Google Scholar
13. Martinelli, A., Tomatis, N. and Siegwart, R., “Simultaneous localization and odometry self calibration for mobile robot,” Auton. Robot 22, 75–85 (2007).Google Scholar
14. Jensfelt, P. and Christensen, H. I., “Pose tracking using laser scanning and minimalistic environmental models,” IEEE Trans. Robot. Autom. 17 (2), 138–147 (2001).Google Scholar
15. Kane, T. R. and Levinson, D. A., Dynamics, Theory and Applications (McGraw-Hill, NY, 2005).Google Scholar
16. Havangi, R., Nekoui, M. A. and Teshnehlab, M., “Adaptive neuro-fuzzy extended kalman filtering for robot localization,” Int. J. Comput. Sci. 7 (2), 15–23 (Mar. 2010).Google Scholar
17. Tur, M. and Maria, J., “Onto Computing the Uncertainty for the Odometry Pose Estimate of a Mobile Robot,” 12th IEEE International Conference on Emerging Technologies and Factory Automation, Patras, Greece (2007) pp. 1340–1345.Google Scholar
18. Stepanov, O. A., Vasiliev, V. A. and Osipov, A. V., “Fuzzy and Bayesian algorithms for a nonlinear navigation estimation problem,” Gyroscopy Navig. 1 (4), 272–278 (2010).Google Scholar
19. Bittel, O. and Blaich, M., “Mobile Robot Localization Using Beacons and the Kalman Filter Technique for the Eurobot Competition. Proceedings,” Research and Education in Robotics - EUROBOT 2011. Communications in Computer and Information Science Volume 161 2011. International Conference, Prague, Czech Republic (Jun. 15–17, 2011).Google Scholar