Localization of legged robots combining a fuzzy-Markov method and a population of extended Kalman filters

This paper presents a new approach to robot vision-based self-localization in dynamic and noisy environments for legged robots when efficiency is a strong requirement. The major contribution of this paper is the improvement of a Markovian method based on a fuzzy occupancy grid (FMK). Our proposal combines FMK with a population of Extended Kalman Filters, making the complete algorithm both robust and accurate while keeping its computational cost bounded. Two different strategies have been designed to combine both the methods. They have been tested in the RoboCup environment and quantitatively compared with other approaches in several experiments with the real robot.