A probabilistic framework for global localization with segmented planes

This paper proposes a novel approach to global localization using high-level features. The new probabilistic framework enables to incorporate uncertain localization cues into a probability distribution that describes the likelihood of the current robot pose. We use multiple triplets of planes segmented from RGB-D data to generate this probability distribution and to find the robot pose with respect to a global map of planar segments. The algorithm can be used for global localization with a known map or for closing loops with RGB- D data. The approach is validated in experiments using the publicly available NYUv2 RGB-D dataset and our new dataset prepared for testing localization on plane-rich scenes.