Improving Robot Self-localization Using Landmarks’ Poses Tracking and Odometry Error Estimation
In this article the classical self-localization approach is improved by estimating, independently from the robot’s pose, the robot’s odometric error and the landmarks’ poses. This allows using, in addition to fixed landmarks, dynamic landmarks such as temporally local objects (mobile objects) and spatially local objects (view-dependent objects or textures), for estimating the odometric error, and therefore improving the robot’s localization. Moreover, the estimation or tracking of the fixed-landmarks’ poses allows the robot to accomplish successfully certain tasks, even when having high uncertainty in its localization estimation (e.g. determining the goal position in a soccer environment without directly seeing the goal and with high localization uncertainty). Furthermore, the estimation of the fixed-landmarks’ pose allows having global measures of the robot’s localization accuracy, by comparing the real map, given by the real (a priori known) position of the fixed-landmarks, with the estimated map, given by the estimated position of these landmarks. Based on this new approach we propose an improved self-localization system for AIBO robots playing in a RoboCup soccer environment, where the odometric error estimation is implemented using Particle Filters, and the robot’s and landmarks’ poses are estimated using Extended Kalman Filters. Preliminary results of the system’s operation are presented.
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- 1.Ruiz-del-Solar, J., et al.: UChile Kiltros 2007 Team Description Paper. In: RoboCup 2007 Symposium, Atlanta, USA, July 9 – 10 (CD Proceedings) (2007)Google Scholar
- 2.Guerrero, P., Ruiz-del-Solar, J., Palma-Amestoy, R.: Spatiotemporal Context in Robot Vision: Detection of Static Objects in the RoboCup Four Legged League. In: Proc. 1st Int. Workshop on Robot Vision(in 2nd Int. Conf. on Computer Vision Theory and Appl. – VISAPP 2007), Barcelona, Spain, March 8 – 11, pp. 136–148 (2007)Google Scholar
- 4.Welch, G., Bishop, G.: An introduction to the Kalman Filter. Tech. Report TR 95-041 (Update May 23, 2003), Department of Computer Science, Univ. of North Carolina at Chapel Hill (2003)Google Scholar
- 6.Kalman, R.E.: A New Approach to Linear Filtering and Prediction Problems. Trans. of the ASME - Journal of Basic Engineering 82, 35–45 (1960)Google Scholar
- 7.Siegwart, R., Nourbakhsh, I.: Introduction to Autonomous Mobile Robots. MIT Press (2004)Google Scholar
- 8.Polani, D., Browning, B., Bonarini, A., Yoshida, K. (eds.): Proc. of the RoboCup 2003 Symposium, Padova, Italy, July 9 - 11 (2003) (CD Proceedings)Google Scholar
- 9.Nardi, D., Riedmiller, M., Sammut, C., Santos Victor, J. (eds.): Proc. of the RoboCup 2004 Symposium, Lisbon, Portugal, July 4 – 5 (2004) (CD Proceedings)Google Scholar
- 10.Montiel, J., Davison, A.: A Visual Compass based on SLAM. In: Proc. of the ICRA 2006, Orlando, USA, May 15-19, pp. 1917–1922 (2006)Google Scholar
- 11.Wang, C., Thorpe, C., Thrun, S.: Online Simultaneous Localization and Mapping with Detection and Tracking of Moving Objects: Theory and Results from a Ground Vehicle in Crowded Urban Areas. In: Proc. of the ICRA 2003, Taipei, Taiwan, May 12-17, pp. 842–849 (2003)Google Scholar
- 13.Konolige, K., Agrawal, M.: Frame-frame Matching for Realtime Consistent Visual Mapping. In: Proc. 1st Int. Workshop on Robot Vision (in 2nd Int. Conf. on Computer Vision Theory and Appl. – VISAPP 2007), Barcelona, Spain, March 8 – 11, pp. 13–26 (2007)Google Scholar