Robotics: Science and Systems I

Discriminative Training of Kalman Filters

Pieter Abbeel, Adam Coates, Michael Montemerlo, Andrew Y. Ng, Sebastian Thrun

Abstract: Kalman filters are a workhorse of robotics and are routinely used in state-estimation problems. However, their performance critically depends on a large number of modeling parameters which can be very difficult to obtain, and are often set via significant manual tweaking and at a great cost of engineering time. In this paper, we propose a method for automatically learning the noise parameters of a Kalman filter. We also demonstrate on a commercial wheeled rover that our Kalman filter's learned noise covariance parameters-obtained quickly and fully automatically-significantly outperform an earlier, carefully and laboriously hand-designed one.

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Bibtex:

@INPROCEEDINGS{ Abbeel-RSS-05,
    AUTHOR    = {Pieter Abbeel and Adam Coates and Michael Montemerlo 
                 and Andrew Y. Ng and Sebastian Thrun},
    TITLE     = {Discriminative Training of Kalman Filters},
    BOOKTITLE = {Proceedings of Robotics: Science and Systems},
    YEAR      = {2005},
    ADDRESS   = {Cambridge, USA},
    MONTH     = {June},
    DOI       = {10.15607/RSS.2005.I.038} 
}