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RadarHD creates high resolution lidar-like point clouds from just a single-chip, cheap mmWave radar. This enables high quality perception even in vision/lidar denied scenarios such as in smoke or fog. For example: think of a futuristic fire fighting robot performing search and rescue in a smoky environment. This repository hosts files pertaining to this project that appeared in IEEE ICRA 2023.

Paper link | Demo link | Project website

RadarHD Overview

Pre-requisites

Repository structure

  • install.sh installs all dependencies.
  • train_radarhd.py and test_radarhd.py are used for training and testing our models.
  • Pre-trained model is stored in logs/13_1_20220320-034822/. This model was trained using radar-lidar images dataset in dataset_5/.
  • train_test_utils/ contains model, loss and dataloading definitions for training and testing.
  • eval/ contains scripts for evaluating RadarHD's generated upsampled radar images.
  • create_dataset/ contains scripts that show our pre-ML radar and lidar processing on raw sensor data. Use this only for creating your own radar-lidar images dataset (similar to dataset_5) to train with our models.

Usage

  • Create a Docker environment

      sudo docker run -it --rm --gpus all --shm-size 8G -v project_root:/radarhd/ pytorch/pytorch bash
    
  • Install all dependencies

      cd /radarhd/
      sh install.sh
    
    • When prompted, be sure to setup time zone as US Eastern. This avoids any time zone related issues that show up during dataset creation.
  • For testing on pre-trained model logs/13_1_20220320-034822/ and test images in dataset_5/test/

      python3 test_radarhd.py
    
    • For testing with other models and datasets, modify the constants in test_radarhd.py.
    • To test on CPU, make sure to use CPU device.
  • For training using params similar to logs/13_1_20220320-034822/ and train images in dataset_5/train/

      python3 train_radarhd.py
    
    • For training with your own params and datasets, modify the constants in train_radarhd.py
  • For evaluating the output of test_radarhd.py:

    • Executing test_radahd.py will create generated upsampled radar and ground truth lidar images in polar format for all the test data in the corresponding log folder. (Default: logs/13_1_20220320-034822/test_imgs/)

    • Convert polar images to cartesian.

        cd ./eval/
        python3 pol_to_cart.py
      
    • Convert cartesian images to point cloud for point cloud error evaluation.

        python3 image_to_pcd.py
      
    • Visualize the generated point clouds for qualitative comparison in Matlab.

       pc_vizualize.m
      
    • Generate quantitative point cloud comparison in Matlab (similar to eval/cdf.jpg)

        pc_compare.m
      

Citation

If you found this work useful, please consider citing this work as:

@INPROCEEDINGS{10161429,
author={Prabhakara, Akarsh and Jin, Tao and Das, Arnav and Bhatt, Gantavya and Kumari, Lilly and Soltanaghai, Elahe and Bilmes, Jeff and Kumar, Swarun and Rowe, Anthony},
booktitle={2023 IEEE International Conference on Robotics and Automation (ICRA)}, 
title={High Resolution Point Clouds from mmWave Radar}, 
year={2023},
volume={},
number={},
pages={4135-4142},
doi={10.1109/ICRA48891.2023.10161429}}