When: Thurs, Oct 17th 2019, 1:00p
Where: Rose St Building Seminar Area
Title: Estimating noise covariance in filters using autocovariance least squares
Abstract: The extended Kalman filter is an extremely useful tool that is widely applied in robotics. In practice we often read the sensor noise covariance matrix off a datasheet and make some sort of educated (or not) guess about the process noise covariance. Several techniques exist for estimating these noise matrices in a principled way, but all have drawbacks around computation or accuracy, one of the most effective approaches is known as autocovariance least squares (ALS). In this talk I will be presenting the application of ALS to estimating these covariances in a visual servoing problem.
Bio: Jasper Brown completed a bachelor of mechatronic engineering at the University of Sydney in 2016 and is currently a PhD student with the ACFR focusing on active perception for manipulation.