Monte Carlo Localization With Mixture Proposal Distribution

Proceedings of the 17th National Conference on Artificial Intelligence and 12th Conference on Innovative Applications of Artificial Intelligence, AAAI 2000

Volumn , Issue , 2000, Pages 859-865

  Thrun, Sebastian ^a   Fox, Dieter ^a   Burgard, Wolfram ^b  

^a Carnegie Mellon University   (United States)

^b UNIVERSITY OF FREIBURG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

MOBILE ROBOTS; ROBOT APPLICATIONS;

BAYESIAN ALGORITHMS; FILTER APPLICATIONS; MOBILE ROBOT LOCALIZATION; MONTE CARLO LOCALIZATION; MONTE CARLO SAMPLING; PARTICLE FILTER; PHYSICAL ROBOTS; PROPOSAL DISTRIBUTION; SAMPLING STEPS;

MONTE CARLO METHODS;

EID: 85158081282     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. J.L. Bentley. Multidimensional divide and conquer. Communications of the ACM, 23(4), 1980.
2. F. Dellaert, W. Burgard, D. Fox, and S. Thrun. Using the condensation algorithm for robust, vision-based mobile robot localization. CVPR-99.
3. J. Denzler, B. Heigl, and H. Niemann. Combining computer graphics and computer vision for probabilistic self-localization. Rochester University, Internal Report, 1999.
4. A Doucet. On sequential simulation-based methods for Bayesian filtering. TR CUED/F-INFENG/TR 310, Cambridge Univ., 1998.
5. S. Engelson and D. McDermott. Error correction in mobile robot map learning. ICRA-92.
6. D. Fox, W. Burgard, F. Dellaert, and S. Thrun. Monte carlo localization: Efficient position estimation for mobile robots. AAAI-99.
7. D. Fox, W. Burgard, and S. Thrun. Markov localization for mobile robots in dynamic environments. JAIR 11, 1999.
8. R. Fung and B. Del Favero. Backward simulation in bayesian networks. UAI-94.
9. M. Isard and A. Blake. Condensation: conditional density propagation for visual tracking. IJCV, In Press.
10. K. Kanazawa, D. Koller, and S.J. Russell. Stochastic simulation algorithms for dynamic probabilistic networks. UAI-95.
11. S. Lenser and M. Veloso. Sensor resetting localization for poorly modelled mobile robots. ICRA-2000, to appear.
12. J. Liu and R. Chen. Sequential monte carlo methods for dynamic systems. Journal of the American Statistical Association, 93, 1998.
13. P.S. Maybeck. The Kalman filter: An introduction to concepts. In Autonomous Robot Vehicles. Springer, 1990.
14. A. W. Moore. Efficient Memory-based Learning for Robot Control. PhD thesis, Cambridge Univ., 1990.
15. M. Pitt and N. Shephard. Filtering via simulation: auxiliary particle filter. Journal of the American Statistical Association, 1999.
16. L.R. Rabiner and B.H. Juang. An introduction to hidden markov models. IEEE ASSP Magazine, 1986.
17. M.A. Tanner. Tools for Statistical Inference. Springer, 1993.
18. S. Thrun, M. Bennewitz, W. Burgard, A.B. Cremers, F. Dellaert, D. Fox, D. Hähnel, C. Rosenberg, N. Roy, J. Schulte, and D. Schulz. MINERVA: A second generation mobile tourguide robot. ICRA-99.