ibrahimhroob / sps

Generalizable Stable Points Segmentation for 3D LiDAR Scan-to-Map Long-Term Localization

Generalizable Stable Points Segmentation for 3D LiDAR Scan-to-Map Long-Term Localization

Our method segments stable and unstable points in 3D LiDAR scans exploiting the discrepancy of scan voxels and overlapping map voxels (highlighted as submap voxels). We showcase two LiDAR scans captured during separate localization sessions within an outdoor vineyard. The scan on the left depicts the vineyard state in April, while the scan on the right reveals environmental changes in plant growth in June

Click here for qualitative results!

sps.mp4

Our stable points segmentation prediction for three datasets. The stable points are depicted in black, while the unstable points are represented in red.

Building the Docker image

We provide a Dockerfile and a docker-compose.yaml to run all docker commands.

IMPORTANT To have GPU access during the build stage, make nvidia the default runtime in /etc/docker/daemon.json:

```yaml
{
    "runtimes": {
        "nvidia": {
            "path": "/usr/bin/nvidia-container-runtime",
            "runtimeArgs": []
        } 
    },
    "default-runtime": "nvidia" 
}
```
Save the file and run ```sudo systemctl restart docker``` to restart docker.

To build the image, simply type the following in the terminal:

bash build_docker.sh

Once the build process finishes, initiate the Docker container in detached mode using Docker Compose from the project directory:

docker-compose up -d # or [docker compose up -d] for older versions

Usage Instructions

Training

To train the model with the parameters specified in config/config.yaml, follow these steps:

1. Export the path to the dataset (This step may be necessary before initiating the container):

   export DATA=path/to/dataset

2. Initiate training by executing the following command from within the container:

   python scripts/train.py

Segmentation Metrics

To evaluate the segmentation metrics for a specific sequence:

python scripts/predict.py -seq <SEQ ID>

This command will generate reports for the following metrics:

• uIoU (unstable points IoU)
• Precision
• Recall
• F1 score

Localization

Install and build the following packages in your catkin_ws:

cd </path/to/catkin_ws>/src
git clone https://github.com/koide3/ndt_omp
git clone https://github.com/SMRT-AIST/fast_gicp --recursive 
git clone https://github.com/koide3/hdl_global_localization 
git clone --branch SPS https://github.com/ibrahimhroob/hdl_localization.git
cd ..
catkin build
source devel/setup.bash

Then, the localization experiment can be run using a single command:

bash exp_pipeline/loc_exp_general.bash

In order to calculate the localization metrics please install evo library

Data

You can download the post-processed and labelled BLT dataset and the parking lot of NCLT dataset from the proveded links.

The weights of our pre-trained model can be downloaded as well.

Here the general structure of the dataset:

DATASET/
├── maps
│   ├── base_map.asc
│   ├── base_map.asc.npy
│   └── base_map.pcd
└── sequence
    ├── SEQ
    │   ├── map_transform
    │   ├── poses
    |   |   ├── 0.txt
    |   |   └── ...
    │   └── scans
    |       ├── 0.npy
    |       └── ...
    |
    └── ...

Publication

If you use our code in your academic work, please cite the corresponding paper:

@article{hroob2024ral,
  author = {I. Hroob* and B. Mersch* and C. Stachniss and M. Hanheide},
  title = {{Generalizable Stable Points Segmentation for 3D LiDAR Scan-to-Map Long-Term Localization}},
  journal = {IEEE Robotics and Automation Letters (RA-L)},
  volume = {9},
  number = {4},
  pages = {3546-3553},
  year = {2024},
  doi = {10.1109/LRA.2024.3368236},
}

Acknowledgments

This implementation is inspired by 4DMOS.

License

This project is free software made available under the MIT License. For details see the LICENSE file.