This page is the implementation of a pre-print, implemented by PyTorch.
The files in matlab/RobotCar_data and matlab/MulRan_data can help you generate scancontext of radar and lidar submaps. Also, the generation of lidar submaps is included.
The train_disco_lidar_quad.py is used for training lidar-to-lidar DiSCO.
The train_disco_radar_quad.py is used for training radar-to-radar DiSCO.
The train_joint_radar_lidar.py is used for training L2L, R2R and R2L jointly based on DiSCO implementation.
The trained models are listed in the trained_models respectively.
Please use the files in inference folder.
In addition, the matlab/evaluate_recall@1 contains the files to calculate the recall@1 for place recognition evaluation.
Multi-session place recognition: radar-to-lidar in different days of Mulran-Riverside
If you use our code in an academic work or inspired by our method, please consider citing the following:
@article{yin2021radar,
title={Radar-to-Lidar: Heterogeneous Place Recognition via Joint Learning},
author={Yin, Huan and Xu, Xuecheng and Wang, Yue and Xiong, Rong},
journal={Frontiers in Robotics and AI},
year={2021},
status={Accept}
}
And also, another related implementation is avaliable at DiSCO.
We also propose an end-to-end radar tracking method on lidar maps. Please refer to RaLL for details.
Make the original data and lidar filter files avaliable.