In this work we analyze robot motion given from the UTIAS Multi-Robot Dataset. The dataset contains recordings of robots wander- ing in a conned environment with randomly spaced static landmarks. After some preprocessing of the data, an algorithm based on the Ex- tended Kalman Filter is developed to determine the positions of robots at every instant of time using the positions of the landmarks. The al- gorithm takes into account the asynchronous time steps and the sparse measurement data to develop its estimates. These estimates are then compared with the groundtruth data provided in the same dataset. Fur- thermore several methods of noise estimation are tested, which improve the error of the estimate for some robots.
robot localization; Extended Kalman Filterl noise estimation; real-world data