perceptual-kiss-slam 0.0.3
KISS-SLAM: A Simple, Robust, and Accurate 3D LiDAR SLAM System With Enhanced Generalization Capabilities
Requires Python: >=3.8
Keywords: LiDAR, SLAM, Mapping

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    <h1>KISS-SLAM</h1>
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    <br />
    <br />
    <a href="https://github.com/PRBonn/kiss-slam/blob/main/README.md#Install">Install</a>
    <span>&nbsp;&nbsp;•&nbsp;&nbsp;</span>
    <a href="https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/kiss2025iros.pdf">Paper</a>
    <span>&nbsp;&nbsp;•&nbsp;&nbsp;</span>
    <a href=https://github.com/PRBonn/kiss-slam/issues>Contact Us</a>
  <br />
  <br />

[KISS-SLAM](https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/kiss2025iros.pdf) is a simple, robust, and accurate 3D LiDAR SLAM system that **just works**.


![motivation](https://github.com/user-attachments/assets/66c3e50f-009a-4a36-9856-283a895c300f)


</div>

<hr />

## Install

```
pip install kiss-slam
```

## Running the system
Next, follow the instructions on how to run the system by typing:
```
kiss_slam_pipeline --help
```

This should print the following help message:

![help](https://github.com/user-attachments/assets/5a6fe624-2aaf-466f-8a18-51039b794000)

### Config
You can generate a default `config.yaml` by typing:

```
kiss_slam_dump_config
```

which will generate a `kiss_slam.yaml` file. Now, you can modify the parameters and pass the file to the `--config` option when running the `kiss_slam_pipeline`.

Suggestion for indoor applications:
1. Reduce the `odometry.preprocessing.max_range` to 50.0, this will automatically reduce the `voxel_size` to 0.5.
2. Reduce the `local_mapper.splitting_distance` to a suitable distance based on the scale of the indoor environment.

### Install Python API (developer mode)
For development purposes:

```
sudo apt install git python3-pip libeigen3-dev libsuitesparse-dev
python3 -m pip install --upgrade pip
git clone https://github.com/PRBonn/kiss-slam.git
cd kiss-slam
make editable
```

### Paper Results
As we decided to continue developing **KISS-SLAM** beyond the scope of the IROS paper, we created a ``git tag`` so that researchers can consistently reproduce the results presented in the publication. To checkout at this tag, you can run the following:
```sh
git checkout IROS25
```
Our development aims to surpass the performance of **KISS-SLAM** beyond the original results presented in the paper.

## Citation
If you use this library for any academic work, please cite our original paper:
```bib
@article{kiss2025arxiv,
  author   = {T. Guadagnino and B. Mersch and S. Gupta and I. Vizzo and G. Grisetti and C. Stachniss},
  title    = {{KISS-SLAM: A Simple, Robust, and Accurate 3D LiDAR SLAM System With Enhanced Generalization Capabilities}},
  journal  = {arXiv preprint},
  year     = 2025,
  volume   = {arXiv:2503.12660},
  url      = {https://arxiv.org/pdf/2503.12660},
}
```

## Acknowledgements
This project builds on top of [KISS-ICP](https://github.com/PRBonn/kiss-icp), [MapClosures](https://github.com/PRBonn/MapClosures), and [g2o](https://github.com/RainerKuemmerle/g2o).

## Contributing

We envision KISS-SLAM as a community-driven project. We love to see how the project is growing, thanks to the contributions from the community. We would love to see your face in the list below; open a Pull Request!

<a href="https://github.com/PRBonn/kiss-slam/graphs/contributors">
  <img src="https://contrib.rocks/image?repo=PRBonn/kiss-slam" />
</a>

## Contact Us
For questions or feedback:
- GitHub Issues: https://github.com/PRBonn/kiss-slam/issues