The more the better? A discussion about line features for self-localization

Abstract

The paper deals with the role of line features in mobile robot self-localization, when an extended Kalman filter is adopted for position tracking. First, a theoretical analysis is introduced, showing how the "length" of each extracted line (i.e., the number of the contributing range measurements) affects the localization accuracy. Second, a novel approach that takes into account the main findings of the theoretical analysis is considered. Finally, experimental results are used to validate the system.