Radar-to-Lidar: Heterogeneous Place Recognition via Joint Learning

Overview

radar-to-lidar-place-recognition

This page is the coder of a pre-print, implemented by PyTorch.

If you have some questions on this project, please feel free to contact Huan Yin [email protected] .

Method

Data

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.

Training

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.

Inference

Please use the files in inference folder.

Evaluation

In addition, the matlab/[email protected] contains the files to calculate the [email protected] for place recognition evaluation.

Case Example

Multi-session place recognition: radar-to-lidar in different days of Mulran-Riverside

Citation

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.

TODO

Make the original data and lidar filter files avaliable.

Owner
Huan Yin
PhD Candidate in Robotics Lab, Zhejiang Univeristy, China
Huan Yin
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