Extended Kalman Filter and Sigma Point Filter Approaches to Adaptive Filtering

Abstract

We compare the state estimation performance of various filters using experimental data. The experiment, a mobile robot driving on a planar surface, provides noisy odometry and laser rangefinder measurements, along with groundtruth provided by an accurate motion capture system. The sensor noise statistics are not purely normal. We investigate the performance of standard extended Kalman and sigma point filters, and compare their performance to adaptive extended Kalman and adaptive sigma point filters. The adaptive filters update the noise covariance matrices based on the measurements available at a given time step.