Novel mobile robot simultaneous loclization and mapping using rao-blackwellised particle filter

International Journal of Advanced Robotic Systems

Volumn 3, Issue 3, 2006, Pages 231-238

  Li, Maohai ^a   Hong, Bingrong ^a   Luo, Ronghua ^b  

^a HARBIN INSTITUTE OF TECHNOLOGY   (China)

^b SOUTH CHINA UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

Mobile robot; Rao Blackwellised particle filter; Scale Invariant Feature Transform; Simultaneous localization and mapping; Vision

Indexed keywords

RAO-BLACKWELLISED PARTICLE FILTERS; SCALE INVARIANT FEATURE TRANSFORMS; SIMULTANEOUS LOCALIZATION AND MAPPING;

COMPUTATIONAL COMPLEXITY; COMPUTER VISION; COSTS; IMAGE PROCESSING; KALMAN FILTERING;

MOBILE ROBOTS;

EID: 33750180790     PISSN: 17298806     EISSN: 17298814     Source Type: Journal    
DOI: None     Document Type: Article

References (13)

1. Chong, K. S. & Kleeman, L. (1999). Feature-based mapping in real large scale environments using an ultrasonic array, International Journal of Robotics Research, Vol. 18, No. 1, pp. 3-19, ISSN 0278-3649
2. Choset, H. & Nagatani, K. (2001). Topological simultaneous localization and mapping (SLAM): Toward exact localization without explicit localization, IEEE Trans. Robot. Automat., Vol.17, No. 2, pp. 125-137, May 21-26, Seoul, Korea
3. Dellaert, F., Fox, D., Burgard, W. & Thrun, S. (1999). Monte Carlo localization for mobile robots, in Proc. IEEE Int. Conf. Robotics and Automation (ICRA), Vol. 2, pp. 10-15, Detroit, MI, USA
4. Doucet, A. (1998). On sequential simulation-based methods for Bayesian filtering, Technical report. Signal Processing Group, Departement of Engeneering, University of Cambridge
5. Kortenkamp, D., Bonasso, R. P. & Murphy, R. (1998). AI-based Mobile Robots: Case studies of successful robot systems, MIT Press, Cambridge
6. Liu, J. S. & Chen, R. (1998). Sequential Monte Carlo methods for dynamical systems, J.Amer.Statist.Assoc.,Vol. 93, pp. 1032-1044
7. Lowe, D. (2004). Distinctive image features from scale-invariant keypoints, Int. J. of Computer Vision, Vol. 60, No. 2, pp. 91-110, ISSN 0920-5691
8. Ma S. D. & Zhang ZH. Y. (1998). Computer vision-computational theories and algorithm, Science Press, Beijing, pp.52-79 (in Chinese)
9. Merwe, R., Doucet, A., Freitas N. & Wan, E. (2000). The Unscented Particle Filter, Technical Report CUED/FINFENG/TR 380, Cambridge University, Engineering Department
10. Montemerlo, M. & Thrun, S. (2003). Simultaneous localization and mapping with unknown data association using FastSLAM, in Proc. IEEE Int. Conf. Robotics and Automation (ICRA), Taipei, China
11. Moore, A.W. (1991). An introductory tutorial on kd-trees, Robotics Institute, Carnegie Mellon University, Pittsburgh, Technical Report No. 209, Computer Laboratory, University of Cambridge
12. Murphy, K. & Russell, S. (2001). Rao-blackwellized particle filtering for dynamic bayesian networks, in Sequential monte carlo methods in practice, Springer Verlag
13. Schulte, A. C. & Adams, W. (1998). Continuous localization using evidence grids, in Proc. IEEE Int. Conf. Robotics and Automation (ICRA), pp.2833-2839, May 16-20, Leuven, Belgium