RaLL

RaLL: End-to-end Radar Localization on Lidar Map Using Differentiable Measurement Model

Video: YouTube | Bilibili

**NOTE: This repo is built in 2020. Please use an old PyTorch version to run it, e.g., torch==1.5.0**

Folders

• data/maps (>80Mb)

point cloud maps and images with resolution of 0.25m/pixel

We have uploaded the radar scan images of Seq02 on Google Drive.

• data/gt_poses

groud truth poses for evaluation

• data/odom

odometry data via ICP

• ekf_filter

differetiable ekf implementation

• loader

dataLoader for training and inference

• loss

cross entropy loss (L1) and squared error loss (L2)

• models

Pre-trained models of RaLL

• network

feature extraction network and patch network

• test_py

test pose tracking on RobotCar and MulRan

To train RaLL

Please use the train_rall_L12.py and train_rall_L3.py. Please modify the data path in the python files.

Publication

If you use the data or code in an academic work, or inspired by our method, please consider citing the following:

@article{yin2021rall,
  title={RaLL: End-to-end Radar Localization on Lidar Map Using Differentiable Measurement Model},
  author={Yin, Huan and Chen, Runjian and Wang, Yue and Xiong, Rong},
  journal={IEEE Transactions on Intelligent Transportation Systems},
  year={2021},
  publisher={IEEE}
}

We also propose a heterogeneous place recognition method with radar and lidar. Please refer to Radar-to-Lidar for details.