Discriminative Training of Kalman Filters

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. Download: 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} }