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Learning an Outlier-Robust Kalman Filter


Technical Report


We introduce a modified Kalman filter that performs robust, real-time outlier detection, without the need for manual parameter tuning by the user. Systems that rely on high quality sensory data (for instance, robotic systems) can be sensitive to data containing outliers. The standard Kalman filter is not robust to outliers, and other variations of the Kalman filter have been proposed to overcome this issue. However, these methods may require manual parameter tuning, use of heuristics or complicated parameter estimation procedures. Our Kalman filter uses a weighted least squares-like approach by introducing weights for each data sample. A data sample with a smaller weight has a weaker contribution when estimating the current time step?s state. Using an incremental variational Expectation-Maximization framework, we learn the weights and system dynamics. We evaluate our Kalman filter algorithm on data from a robotic dog.

Author(s): Ting, J. and Theodorou, E. and Schaal, S.
Book Title: CLMC Technical Report: TR-CLMC-2007-1
Year: 2007

Department(s): Autonomous Motion
Bibtex Type: Technical Report (techreport)

Address: Los Angeles, CA
Cross Ref: p10118
Note: clmc

Links: PDF


  title = {Learning an Outlier-Robust Kalman Filter},
  author = {Ting, J. and Theodorou, E. and Schaal, S.},
  booktitle = {CLMC Technical Report: TR-CLMC-2007-1},
  address = {Los Angeles, CA},
  year = {2007},
  note = {clmc},
  crossref = {p10118}