DARPA Grand Challenge:
http://en.wikipedia.org/wiki/Darpa_grand_challenge
http://www.darpa.mil/grandchallenge/index.asp
History of autonomous vehicles:
http://www.idsia.ch/~juergen/robotcars.html
Introduction to robot mapping with statistics:
http://www.cs.washington.edu/homes/fox/ecai-tutorial/ecai-02.htm
Computer Vision and Stat by Yuille
http://www.stat.ucla.edu/~yuille/courses/Stat238/
Lecture Notes on Probability and Statistics by D. C. Agnew and C. Constable
http://mahi.ucsd.edu/cathy/Classes/SIO223/Part1/
Robots Incorporated
http://www.spectrum.ieee.org/aug07/5391
PBS did a NOVA show on the race and showed it on TV
http://www.pbs.org/wgbh/nova/darpa/program.html
Mobile robot localization with particle filters (Ch. 8 in Thrun et al. book) (Nagesh)
Robot mapping with particle filters [4] (Nagesh)
Shape similarity of contour parts with subsequence matching (Suzan)
Learning good contour parts (Xin)
Image Segmentation with EM (Chapter 16 in Forsyth and Ponce book) and [9]
Range data analysis (Chapter 21 in Forsyth and Ponce book)
Contour grouping with particle filters (Thomas)
Contour tracking with particle filters [15]
Object Perception as Bayesian Inference [10, 14]
Object Detection [11,13]
Concept Learning [16]
Activity Detection in Videos [17] (Riu)
David A. Forsyth and Jean Ponce . Computer Vision: A Modern Approach. Prentice Hall 2003. ISBN 0-13-085198-1 HTML Version
Richard O. Duda, Peter E. Hart, and David G. Stork. Pattern Classification. (2nd ed.), Wiley, ISBN: 0-471-05669-3 website
Sebastian Thrun , Wolfram Burgard, Dieter Fox. Probabilistic Robotics. MIT Press, 2005, ISBN 0262201623
[1] Sebastian Thrun et al., Stanley: The Robot that Won the DARPA Grand Challenge. Journal of Field Robotics 23(9), 661–692, 2006. pdf
[2] Ernst D. Dickmanns. Vehicles Capable of Dynamic Vision. IJCAI 1997. pdf
[3] Pierre Lamon, et al. Mapping with an Autonomous Car. In Proc. of the Workshop on Safe Navigation in Open and Dynamic Environments at the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), Beijing, China, 2006. paper
[4] Giorgio Grisetti, Cyrill Stachniss, and Wolfram Burgard. Improved Techniques for Grid Mapping with Rao-Blackwellized Particle Filters. Transactions on Robotics, Volume 23, pages 34-46, 2007 paper
[5] Dieter Fox. Adapting the Sample Size in Particle Filters Through KLD-Sampling. IJRR 2003. pdf
[6] Dieter Fox, Jeffrey Hightower, Lin Liao, Dirk Schulz, Gaetano Borriello. Bayesian Filters for Location Estimation. PERVASIVE Computing, July-September 2003. pdf
[7] CHRISTOPHE ANDRIEU, NANDO DE FREITAS, ARNAUD DOUCET, MICHAEL I. JORDAN. An Introduction to MCMC for Machine Learning. Machine Learning 50, 5-43, 2003. pdf
[8] M. Sanjeev Arulampalam, Simon Maskell, Neil Gordon, and Tim Clapp. A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking. IEEE Trans. on SIGNAL PROCESSING, VOL. 50, NO. 2, 2002. pdf
[9] Nuno Vasconcelos. From Pixels to Semantic Spaces: Advances in Content-Based Image Retrieval. IEEE Computer, July 2007. pdf
[10] D. Kersten, P. Mamassian, and A.L. Yuille. Object Perception as Bayesian Inference. Annual Review of Psychology, 2003. (file.pdf)
[11] Vittorio Ferrari et al. Groups of Adjacent Contour Segments for Object Detection pdf
[12] GrahamMcNeill and Sethu Vijayakumar. Part-based Probabilistic Point Matching using Equivalence Constraints. NIPS 2006. pdf
[13] Pedro Felzenszwalb and Joshua Schwartz. Hierarchical Matching of Deformable Shapes. CVPR 2007. pdf
[14] J. SULLIVAN, A. BLAKE, M. ISARD, AND J. MACCORMICK. Bayesian Object Localisation in Images. International Journal of Computer Vision 44(2), 111–135, 2001. pdf
[15] Patrick Perez, Andrew Blake, Michel Gangnet. JetStream: Probabilistic Contour Extraction with Particles, ICCV 2001 (II: 524-531). pdf
[16] Shane T. Mueller and Richard M. Shiffrin. Incorporating Connotation of Meaning into Models of Semantic Representation: An Application in Text Corpus Analysis. Cognitive Science Meeting, August 2007. pdf
[17] Alexander Ihler, Jon Hutchins, Padhraic Smyth. Adaptive Event Detection with Time–Varying Poisson Processes. Proc. KDD, Philadelphia, 2006. pdf