A Submap per Perspective - Selecting Subsets for SuPer Mapping that Afford Superior Localization Quality

This paper targets high-precision robot localization. We address a general problem for voxel-based map representations that the expressiveness of the map is fundamentally limited by the resolution since integration of measurements taken from different perspectives introduces imprecisions, and thus reduces localization accuracy. We propose SuPer maps that contain one Submap per Perspective representing a particular view of the environment. For localization, a robot then selects the submap that best explains the environment from its perspective. Our methods serves as an offline refinement step between initial SLAM and deploying autonomous robots for navigation. We evaluate the proposed method on simulated and real-world data that represent an important use case of an industrial scenario with high accuracy requirements in an repetitive environment. Our results demonstrate a significantly improved localization accuracy, up to 46% better compared to localization in global maps, and up to 25% better compared to alternative submapping approaches.