We compare the state estimation performance of various nonlinear filters using experimental data. The experiment, a mobile robot driving on a planar surface, provides noisy odometry and laser rangefinder measurements, while groundtruth is provided by an accurate motion capture system. We investigate the localization accuracy of standard extended Kalman and sigma point filters, and compare their performance with 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 (without using groundtruth data). The groundtruth data is used to assess the performance of each filter. Our results show that the adaptive schemes outperform the equivalent traditional formulations, however, they are slightly more difficult to implement and tune.