Recovery Methods for Fatal Estimation Errors on Monte Carlo Localization

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. The combination of the expansion resetting method and the sensor resetting method, which is a typical 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 verify that the expansion resetting method was effective for recovery from small irregular changes of a robot's pose, and that the combination method could deal with both large and small irregular changes.