Expansion resetting for recovery from fatal error in monte carlo localization - comparison with sensor resetting methods

Abstract

Though Monte Carlo localization is a popular method for mobile robot localization, it requires a method for recovery of large estimation error in itself. In this paper, a recovery method, which is named an expansion resetting method, is newly proposed. A blending of the expansion resetting method and another, which is called the sensor resetting method, is also proposed. We then compared our methods and others in a simulated RoboCup environment. Typical accidents for mobile robots were produced in the simulator during trials. We could grasp the characteristics of each method. Especially, the blending method was robust against the kidnapped robot problems.