  We present a heterogeneous localization framework for solving radar global
localization and pose tracking on pre-built lidar maps. To bridge the gap of
sensing modalities, deep neural networks are constructed to create shared
embedding space for radar scans and lidar maps. Herein learned feature
embeddings are supportive for similarity measurement, thus improving map
retrieval and data matching respectively. In RobotCar and MulRan datasets, we
demonstrate the effectiveness of the proposed framework with the comparison to
Scan Context and RaLL. In addition, the proposed pose tracking pipeline is with
less neural networks compared to the original RaLL.
