Evaluation of gaussian processes and other methods for non-linear regression
Evaluation of gaussian processes and other methods for non-linear regression
Variable resolution particle filter
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Continuous time particle filtering
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Nonstationary Gaussian Process Regression Using Point Estimates of Local Smoothness
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Set-theoretic estimation of hybrid system configurations
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Learning GP-BayesFilters via Gaussian process latent variable models
Autonomous Robots
Robust estimation of multivariate jump-diffusion processes via dynamic programming
Proceedings of the Winter Simulation Conference
A KNN based kalman filter Gaussian process regression
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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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. The extremely low a priori likelihood of failures poses additional challenges for detection algorithms. 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.