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Robust Gaussian Filtering using a Pseudo Measurement

2016

Conference Paper

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Most widely-used state estimation algorithms, such as the Extended Kalman Filter and the Unscented Kalman Filter, belong to the family of Gaussian Filters (GF). Unfortunately, GFs fail if the measurement process is modelled by a fat-tailed distribution. This is a severe limitation, because thin-tailed measurement models, such as the analytically-convenient and therefore widely-used Gaussian distribution, are sensitive to outliers. In this paper, we show that mapping the measurements into a specific feature space enables any existing GF algorithm to work with fat-tailed measurement models. We find a feature function which is optimal under certain conditions. Simulation results show that the proposed method allows for robust filtering in both linear and nonlinear systems with measurements contaminated by fat-tailed noise.

Author(s): Wüthrich, M. and Garcia Cifuentes, C. and Trimpe, S. and Meier, F. and Bohg, J. and Issac, J. and Schaal, S.
Book Title: Proceedings of the American Control Conference
Year: 2016
Month: July

Department(s): Autonomous Motion
Research Project(s): Gaussian Filtering as Variational Inference
Probabilistic Object and Manipulator Tracking
Bibtex Type: Conference Paper (inproceedings)
Paper Type: Conference

Address: Boston, MA, USA
DOI: 10.1109/ACC.2016.7525473
State: Published
URL: http://arxiv.org/abs/1509.04072

Links: Web

BibTex

@inproceedings{wuthrich-rgf-2015,
  title = {Robust Gaussian Filtering using a Pseudo Measurement},
  author = {W{\"u}thrich, M. and Garcia Cifuentes, C. and Trimpe, S. and Meier, F. and Bohg, J. and Issac, J. and Schaal, S.},
  booktitle = {Proceedings of the American Control Conference},
  address = {Boston, MA, USA},
  month = jul,
  year = {2016},
  url = {http://arxiv.org/abs/1509.04072},
  month_numeric = {7}
}