D-GLSNet

The code of Geo-localization with Transformer-based 2D-3D match Network

Introduction

D-GLSNet is a Transformer-based 2D-3D matching network that directly matches the LiDAR point clouds and satellite images through end-to-end learning. Without the need for feature point detection, D-GLSNet provides accurate pixel-to-point association between the LiDAR point clouds and satellite images. And then, we can easily calculate the horizontal offset (Δx,Δy) and angular deviation Δθ(yaw) between them, thereby achieving accurate registration. match

Installation

 # create env
conda create --name D-GLSNet python=3.8 -y
conda activate D-GLSNet

# Install PyTorch and torchvision
pip install torch==1.13.0+cu116 torchvision==0.14.0+cu116 torchaudio==0.13.0 --extra-index-url https://download.pytorch.org/whl/cu116

 # Install packages and other dependencies
pip install -r requirements.txt
python setup.py build develop

Architecture

match

Dateset

Our data is based on Kitti and Kitti-360, and we provide a simple example data on Kitti.

sample
├── train.txt
├── val.txt
├── test.txt
├── Kitti
│   ├── 2011_10_03/2011_10_03_drive_0027_sync/data
│   │   ├── 0000000000.npz
│   │   ├── 0000000001.npz
│   │   ├── ...

Each .npz file contains four arrays:

Point: LiDAR point cloud;
Map_: Google Map of the current frame LiDAR point cloud attachment;
Scale: The scale of Google Maps;
Focal: LiDAR point relative map_ The pixel coordinates of the offset of the center position;

Train

Modify the run_path in the configuration file default_kitti.yaml to modify the output path, and modify the value of the root of data to modify the data directory. Use the following command for training.

CUDA_VISIBLE_DEVICES=0 python train.py pl_DGLSNet ./config/default_kitti.yaml kitt_train

In addition, you need to set use_augmentation to True to enable data augmentation.

Test

Modify pretrained in the default_kitti.yaml file to load the corresponding model. You can download our pretrained models Medium(4,2).ckpt. Use the following command for testing.

CUDA_VISIBLE_DEVICES=0 python train.py pl_DGLSNet ./config/default_kitti.yaml kitt_train --test

Citation

@ARTICLE{10168166,
  author={Li, Laijian and Ma, Yukai and Tang, Kai and Zhao, Xiangrui and Chen, Chao and Huang, Jianxin and Mei, Jianbiao and Liu, Yong},
  journal={IEEE Robotics and Automation Letters}, 
  title={Geo-Localization With Transformer-Based 2D-3D Match Network}, 
  year={2023},
  volume={8},
  number={8},
  pages={4855-4862},
  doi={10.1109/LRA.2023.3290526}}

yzdad / D-GLSNet

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