Heteroscedastic Gaussian processes for data fusion in large scale terrain modeling

Proceedings - IEEE International Conference on Robotics and Automation

Volumn , Issue , 2010, Pages 3452-3459

  Vasudevan, Shrihari ^a   Ramos, Fabio ^a   Nettleton, Eric ^a   Durrant Whyte, Hugh ^a  

^a UNIVERSITY OF SYDNEY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

BEST ESTIMATES; COMPLEX TERRAINS; COMPREHENSIVE MODEL; DATA SETS; GAUSSIAN PROCESS REGRESSION; GAUSSIAN PROCESSES; HETEROSCEDASTIC; LARGE-SCALE TERRAIN; LASER SCANNER DATA; MODELING APPROACH; MULTI RESOLUTION REPRESENTATION; MULTI-MODAL DATA; MULTIPLE DATA; SENSORY INFORMATION; SENSORY MODALITY; SPATIAL DATA; STOCHASTIC PROCESS; SURVEY DATA;

DATA FUSION; GAUSSIAN NOISE (ELECTRONIC); INFORMATION FUSION; LANDFORMS; MODAL ANALYSIS; RANDOM PROCESSES; ROBOTICS; ROBOTS; SECURITY OF DATA; STOCHASTIC MODELS;

GAUSSIAN DISTRIBUTION;

EID: 77955818941     PISSN: 10504729     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ROBOT.2010.5509363     Document Type: Conference Paper

References (20)

1. S. Vasudevan, F. Ramos, E. Nettleton, and H. Durrant-Whyte, "Gaussian Process Modeling of Large Scale Terrain," Journal of Field Robotics, vol. 26(10), 2009, http://www-personal.acfr.usyd.edu.au/shrihari/ svjfr09.pdf.
2. S. Lacroix, A. Mallet, D. Bonnafous, G. Bauzil, S. Fleury, M. Herrb, and R. Chatila, "Autonomous rover navigation on unknown terrains: Functions and Integration," International Journal of Robotics Research (IJRR), vol. 21(10-11), pp. 917-942, 2002.
3. R. Triebel, P. Pfaff, and W. Burgard, "Multi-Level Surface Maps for Outdoor Terrain Mapping and Loop Closing," in International Conference on Intelligent Robots and Systems (IROS), Beijing, China, October 2006.
4. J. Leal, S. Scheding, and G. Dissanayake, "3D Mapping: A Stochastic Approach," in Australian Conference on Robotics and Automation, November 2001.
5. I. Rekleitis, J. Bedwani, D. Gingras, and E. Dupuis, "Experimental Results for Over-the-Horizon Planetary exploration using a LIDAR sensor," in Eleventh International Symposium on Experimental Robotics, July 2008.
6. H. Durrant-Whyte, "A Critical Review of the State-of-the-Art in Autonomous Land Vehicle Systems and Technology," Sandia National Laboratories, USA, Tech. Rep. SAND2001-3685, November 2001.
7. I. D. Moore, R. B. Grayson, and A. R. Ladson, "Digital terrain modelling: A review of hydrological, geomorphological, and biological applications," Hydrological Processes, vol. 5-1, pp. 3-30, 1991.
8. C. E. Rasmussen and C. K. I. Williams, Gaussian Processes for Machine Learning. MIT Press, 2006.
9. P. K. Kitanidis, Introdcution to Geostatistics: Applications in Hydrogeology. Cambridge University Press, 1997.
10. G. Matheron, "Principles of Geostatistics," Economic Geology, vol. 58, pp. 1246-1266, 1963.
11. C. Plagemann, S. Mischke, S. Prentice, K. Kersting, N. Roy, and W. Burgard, "A Bayesian regression approach to terrain mapping and an application to legged robot locomotion," Journal of Field Robotics, vol. 26(10), 2009.
12. C. J. Paciorek and M. J. Schervish, "Nonstationary Covariance Functions for Gaussian Process Regression," in Advances in Neural Information Processing Systems (NIPS) 16, S. Thrun, L. Saul, and B. Schölkopf, Eds. Cambridge, MA: MIT Press, 2004.
13. P. W. Goldberg, C. K. I. Williams, and C. M. Bishop, "Regression with Input-dependent Noise: A Gaussian Process Treatment," in Advances in Neural Information Processing Systems (NIPS) 10, M. I. Jordan, M. J. Kearns, S. A. Solla, and L. Erlbaum, Eds. Cambridge, MA: MIT Press, 1998.
14. M. Yuan and G. Wabha, "Doubly Penalized Likelihood Estimator in Heteroscedastic Regression," Department of Statistics, University of Wisconsin, Madison, WI, USA, Tech. Rep., 2004.
15. Q. Le, A. Smola, and S. Canu, "Heteroscedastic Gaussian Process Regression," in International Conference on Machine Learning (ICML), 2005.
16. K. Kersting, C. Plagemann, P. Pfaff, and W. Burgard, "Most Likely Hetereoscedastic Gaussian Process Regression," in International Conference on Machine Learning (ICML), 2007.
17. M. El-Beltagy and W. Wright, "Gaussian processes for model fusion," in International Conference on Artificial Neural Networks (ICANN), 2001.
18. R. Murray-Smith and B. Pearlmutter, Deterministic and Statistical Methods in Machine Learning, LNAI 3635. Springer-Verlag, 2005, ch. Transformations of Gaussian Process priors, pp. 110-123.
19. N. Lawrence, M. Seeger, and R. Herbrich, "Fast Sparse Gaussian Process Methods: The Informative Vector Machine," in Advances in Neural Information Processing Systems (NIPS) 15, S. T. S. Becker and K. Obermayer, Eds. Cambridge, MA: MIT Press, 2003, pp. 609-616.
20. S. O'Callaghan, F. Ramos, and H. Durrant-Whyte, "Gaussian Process Occupancy Maps Incorporating Sensor and Location Uncertainty," in International Conference on Robotics and Automation (ICRA), 2010.