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CC BY-NC-SA 4.0

CMRNet: Camera to LiDAR-Map Registration (IEEE ITSC 2019)

License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. CC BY-SA 4.0

News

Check out our new paper "CMRNet++: Map and Camera Agnostic Monocular Visual Localization in LiDAR Maps":

2022/05/19

  • Updated for later version of PyTorch 1.4+, and CUDA 11.x

2020/06/24

2020/05/11

  • We released the SLAM ground truth files, see Local Maps Generation.
  • Multi-GPU training.
  • Added requirements.txt

Code

CMRNet Teaser

PyTorch implementation of CMRNet.

This code is a provided "as is", without warranty of any kind. This version only works on GPUs (no CPU version available).

Tested on:

  • Ubuntu 16.04/18.04
  • python 3.6
  • cuda 9/10/11.x
  • pytorch 1.0.1/1.10

Dependencies (this list is not complete):

Installation

Install CUDA, PyTorch, CuPy. Make sure to use the correct cuda version for all the packages.

⚠️ For CUDA 11.x please uncomment line 17 in models/CMRNet/correlation_package/setup.py setup.py#L17

Install the prerequisite packages:

pip install -r requirements.txt

And finally, install the correlation_cuda and the visibility package:

cd models/CMRNet/correlation_package/
python setup.py install
cd ../../..
python setyp.py install

It is recommended to use a dedicated conda environment

Data

We trained and tested CMRNet on the KITTI odometry sequences 00, 03, 05, 06, 07, 08, and 09.

We used a LiDAR-based SLAM system to generate the ground truths.

The Data Loader requires a local point cloud for each camera frame, the point cloud must be expressed with respect to the camera_2 reference frame, BUT (very important) with a different axes representation: X-forward, Y-right, Z-down.

For reading speed and file size we decided to save the point clouds as h5 files.

The directory structure should looks like:

KITTI_ODOMETRY
├── 00
│   ├── image_2
│   │   ├── 000000.png
│   │   ├── 000001.png
│   │   ├── ...
│   │   └── 004540.png
│   ├── local_maps
│   │   ├── 000000.h5
│   │   ├── 000001.h5
│   │   ├── ...
│   │   └── 004540.h5
│   └── poses.csv
└── 03
    ├── image_2
    │   ├── 000000.png
    │   ├── 000001.png
    │   ├── ...
    │   └── 000800.png
    ├── local_maps
    │   ├── 000000.h5
    │   ├── 000001.h5
    │   ├── ...
    │   └── 000800.h5
    └── poses.csv

Local Maps Generation

To generate the h5 files, use the script preprocess/kitti_maps.py with the ground truth files in data/.

In the sequence 08, the SLAM failed to detect a loop closure, so the poses are not coherent around that closure. Therefore, we splitted the map at frame 3000, so to have two coherent maps for that sequence.

python preprocess/kitti_maps.py --sequence 00 --kitti_folder ./KITTI_ODOMETRY/
python preprocess/kitti_maps.py --sequence 03 --kitti_folder ./KITTI_ODOMETRY/
python preprocess/kitti_maps.py --sequence 05 --kitti_folder ./KITTI_ODOMETRY/
python preprocess/kitti_maps.py --sequence 06 --kitti_folder ./KITTI_ODOMETRY/
python preprocess/kitti_maps.py --sequence 07 --kitti_folder ./KITTI_ODOMETRY/
python preprocess/kitti_maps.py --sequence 08 --kitti_folder ./KITTI_ODOMETRY/ --end 3000
python preprocess/kitti_maps.py --sequence 08 --kitti_folder ./KITTI_ODOMETRY/ --start 3000
python preprocess/kitti_maps.py --sequence 09 --kitti_folder ./KITTI_ODOMETRY/

Single Iteration example

Training:

python main_visibility_CALIB.py with batch_size=24 data_folder=./KITTI_ODOMETRY/ epochs=300 max_r=10 max_t=2 BASE_LEARNING_RATE=0.0001 savemodel=./checkpoints/ test_sequence=0

Evaluation:

python evaluate_iterative_single_CALIB.py with test_sequence=00 maps_folder=local_maps data_folder=./KITTI_ODOMETRY/ weight="['./checkpoints/weights.tar']"

Iterative refinement example

Training

python main_visibility_CALIB.py with batch_size=24 data_folder=./KITTI_ODOMETRY/sequences/ epochs=300 max_r=10 max_t=2   BASE_LEARNING_RATE=0.0001 savemodel=./checkpoints/ test_sequence=0
python main_visibility_CALIB.py with batch_size=24 data_folder=./KITTI_ODOMETRY/sequences/ epochs=300 max_r=2  max_t=1   BASE_LEARNING_RATE=0.0001 savemodel=./checkpoints/ test_sequence=0
python main_visibility_CALIB.py with batch_size=24 data_folder=./KITTI_ODOMETRY/sequences/ epochs=300 max_r=2  max_t=0.6 BASE_LEARNING_RATE=0.0001 savemodel=./checkpoints/ test_sequence=0

Evaluation

python evaluate_iterative_single_CALIB.py with test_sequence=00 maps_folder=local_maps data_folder=./KITTI_ODOMETRY/sequences/ weight="['./checkpoints/iter1.tar','./checkpoints/iter2.tar','./checkpoints/iter3.tar']"

Pretrained Model

The weights for the three iterations, trained on the sequences 03, 05, 06, 07, 08 and 09 are available here: Iteration 1 Iteration 2 Iteration 3

Results:

Median
Transl. error

Median
Rotation. error
Iteration 1 0.46 m 1.60°
Iteration 2 0.25 m 1.14°
Iteration 3 0.20 m 0.97°

Paper

"CMRNet: Camera to LiDAR-Map Registration"

If you use CMRNet, please cite:

@InProceedings{cattaneo2019cmrnet,
  author={Cattaneo, Daniele and Vaghi, Matteo and Ballardini, Augusto Luis and Fontana, Simone and Sorrenti, Domenico Giorgio and Burgard, Wolfram},
  booktitle={2019 IEEE Intelligent Transportation Systems Conference (ITSC)},
  title={CMRNet: Camera to LiDAR-Map Registration},
  year={2019},
  pages={1283-1289},
  doi={10.1109/ITSC.2019.8917470},
  month={Oct}
}

If you use the ground truths, please also cite:

@INPROCEEDINGS{Caselitz_2016, 
  author={T. {Caselitz} and B. {Steder} and M. {Ruhnke} and W. {Burgard}}, 
  booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, 
  title={Monocular camera localization in 3D LiDAR maps}, 
  year={2016},
  pages={1926-1931},
  doi={10.1109/IROS.2016.7759304}
}

Acknowledgments

correlation_package was taken from flownet2

PWCNet.py is a modified version of the original PWC-DC network

Contacts

Daniele Cattaneo (cattaneo@informatik.uni-freiburg.de or d.cattaneo10@campus.unimib.it)