Mobile robot localization based on an inaccurate map

Abstract

Proposes a method of global localization on a map with large uncertainty. Since the pose of a mobile robot represented in a global frame of reference might be inconsistent on an inaccurate map, the method represents the robot pose in a local frame attached to a landmark. When the robot finds a new landmark, the robot transforms its pose from the old landmark frame to the new one based on the relative pose between the landmarks. Map errors are represented by probability density functions defined on these relative poses. The paper presents an extension of Markov localization which is augmented so as to incorporate the map errors into data fusion process, and shows experimental results.