Discriminative Training of Kalman Filters
Pieter Abbeel, Adam Coates, Michael Montemerlo, Andrew Y. Ng, Sebastian Thrun · 2005
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.