Learning an Outlier-Robust Kalman Filter

author: Jo-Anne Ting, University of Southern California
published: Jan. 29, 2008,   recorded: September 2007,   views: 12844

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In this talk, 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.

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Reviews and comments:

Comment1 Memming, September 24, 2008 at 7:58 p.m.:

Interesting talk. However, the slides are impossible to see from the video. It would be much better if the slides are available. I just found the slides online when I was in the middle of the talk.

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