Map representations for mobile robot localization

Abstract

Localization is a prerequisite for mobile robots to achieve path planning, autonomous navigation, and decision-making. In localization, maps provide mobile robots with the ability to perceive and model the environment, so the choice of an appropriate map representation has a significant impact on the localization effect of mobile robots. From the perspective of usage matching, this paper divides map representations for mobile robot localization into four categories based on different focuses of describing the environment, which are metric maps, feature maps, topological maps, and semantic maps. The principles, characteristics, and classic cases of these representations are also analyzed in this paper. Furthermore, the applicable working conditions of each type of map representation are summarized to provide effective guidance for mobile robot localization.