Temporal smoothing particle filter for vision based autonomous mobile robot localization

ICINCO 2008 - Proceedings of the 5th International Conference on Informatics in Control, Automation and Robotics

Volumn 1 RA, Issue , 2008, Pages 93-100

  Nisticò, Walter ^a   Hebbel, Matthias ^a  

^a TU DORTMUND UNIVERSITY   (Germany)

Author keywords

Particle filters; Real time systems; Vision based localization

Indexed keywords

AUTONOMOUS MOBILE ROBOTS; COMPUTATIONAL RESOURCES; IMPORTANCE WEIGHTS; INDOOR ENVIRONMENTS; LOCALIZATION FOR MOBILE ROBOTS; MARKOV ASSUMPTIONS; MOBILE ROBOT LOCALIZATIONS; MONTE CARLO; MONTE CARLO LOCALIZATIONS; PARTICLE FILTERS; RE SAMPLINGS; SAMPLING IMPORTANCE RESAMPLING; SENSOR MODELS; SMALL PARTICLES; STATE OF THE ARTS; TEMPORAL SMOOTHING; TIME CONSTRAINTS; VISION BASED LOCALIZATION; VISION-BASED;

AIR FILTERS; AUTOMATION; DISTRIBUTED COMPUTER SYSTEMS; INTERACTIVE COMPUTER SYSTEMS; MOBILE ROBOTS; MONTE CARLO METHODS; NAVIGATION; NONLINEAR FILTERING; REAL TIME SYSTEMS; ROBOT APPLICATIONS; ROBOTICS; SAMPLING; SENSORS; SIGNAL FILTERING AND PREDICTION; TARGET TRACKING; WAVE FILTERS;

ROBOTS;

EID: 55849143667     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (13)

1. Dellaert, F., Fox, D., Burgard, W., and Thrun, S. (1999). Monte carlo localization for mobile robots. In IEEE International Conference on Robotics and Automation (ICRA99).
2. Doucet, A., Godsill, S., and Andrieu, C. (2000). On sequential monte carlo sampling methods for bayesian filtering. Statistics and Computing, 10:197-208.
3. Fox, D. (2003). Adapting the sample size in particle filters through kid-sampling. I. J. Robotic Res., 22(12):985-1004.
4. Fox, D., Hightower, J., Liao, L., Schulz, D., and Borriello, G. (2003). Bayesian Filtering for Location Estimation. PERVASIVE computing, pages 10-19.
5. Geweke, J. (1989). Bayesian inference in econometric models using monte carlo integration. Econometrica, 57(6):1317-39.
6. Gordon, N. J., Salmond, D. J., and Smith, A. F. M. (1993). Novel approach to nonlinear/non-gaussian bayesian state estimation. Radar and Signal Processing, IEE Proceedings F, 140(2):107-113.
7. Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Transactions of the ASME - Journal of Basic Engineering, 82:35-45.
8. Lenser, S. and Veloso, M. (2000). Sensor resetting localization for poorly modelled mobile robots. In Proceedings of the IEEE International Conference on Robotics and Automation, 2000.
9. Nisticò, W. and Röfer, T. (2006). Improving percept reliability in the sony four-legged league. In RoboCup 2005: Robot Soccer World Cup IX, Lecture Notes in Artificial Intelligence, pages 545-552. Springer.
10. Röfer, T. and Jüngel, M. (2003). Vision-Based Fast and Reactive Monte-Carlo Localization. In Proceedings of the 2003 IEEE International Conference on Robotics and Automation, ICRA 2003, September 14-19, 2003, Taipei, Taiwan, pages 856-861. IEEE.
11. Sony Corporation (2004). OPEN-R SDK Model Information for ERS-7. Sony Corporation.
12. Thrun, S., Burgard, W., and Fox, D. (2005). Probabilistic Robotics. Number ISBN 0-262-20162-3. MIT Press.
13. Thrun, S., Fox, D., Burgard, W., and Dellaert, F. (2001). Robust monte carlo localization for mobile robots. Artificial Intelligence, 128(1-2):99-141.