  Relocalization is a fundamental task in the field of robotics and computer
vision. There is considerable work in the field of deep camera relocalization,
which directly estimates poses from raw images. However, learning-based methods
have not yet been applied to the radar sensory data. In this work, we
investigate how to exploit deep learning to predict global poses from Emerging
Frequency-Modulated Continuous Wave (FMCW) radar scans. Specifically, we
propose a novel end-to-end neural network with self-attention, termed RadarLoc,
which is able to estimate 6-DoF global poses directly. We also propose to
improve the localization performance by utilizing geometric constraints between
radar scans. We validate our approach on the recently released challenging
outdoor dataset Oxford Radar RobotCar. Comprehensive experiments demonstrate
that the proposed method outperforms radar-based localization and deep camera
relocalization methods by a significant margin.
