@InProceedings{plagemann07ijcai,
  TITLE     = {Efficient Failure Detection on Mobile Robots Using Particle Filters with Gaussian Process Proposals},
  AUTHOR    = {Plagemann, C. and Fox, D. and Burgard, W.},
  BOOKTITLE = {Proc.~of the Twentieth International Joint Conference on Artificial Intelligence (IJCAI)},
  ADDRESS   = {Hyderabad, India},
  YEAR      = {2007},
  PDFURL       = {http://www.informatik.uni-freiburg.de/~plagem/bib/plagemann07ijcai.pdf},
  ABSTRACT  = {The ability to detect failures and to analyze their causes is one of
the preconditions of truly autonomous mobile robots.  Especially
online failure detection is a complex task, since the effects of
failures are typically difficult to model and often resemble the
noisy system behavior in a fault-free operational mode.  In this
paper we present an approach that applies Gaussian process
classification and regression techniques for learning highly
effective proposal distributions of a particle filter that is
applied to track the state of the system.  As a result, the
efficiency and robustness of the state estimation process is
substantially improved.  In practical experiments carried out with a
real robot we demonstrate that our system is capable of detecting
collisions with unseen obstacles while at the same time estimating
the changing point of contact with the obstacle.}
}