  Place recognition is critical for both offline mapping and online
localization. However, current single-sensor based place recognition still
remains challenging in adverse conditions. In this paper, a heterogeneous
measurements based framework is proposed for long-term place recognition, which
retrieves the query radar scans from the existing lidar maps. To achieve this,
a deep neural network is built with joint training in the learning stage, and
then in the testing stage, shared embeddings of radar and lidar are extracted
for heterogeneous place recognition. To validate the effectiveness of the
proposed method, we conduct tests and generalization experiments on the
multi-session public datasets compared to other competitive methods. The
experimental results indicate that our model is able to perform multiple place
recognitions: lidar-to-lidar, radar-to-radar and radar-to-lidar, while the
learned model is trained only once. We also release the source code publicly:
https://github.com/ZJUYH/radar-to-lidar-place-recognition.
