linefit 1.1.0

linefit ground segmentation algorithm Python bindings

linefit

linefit is a ground segmentation algorithm for 3D point clouds. This repo we setup a python binding for the original C++ code and push to pypi for easy installation through pip install linefit.

Author: C++ code from Lorenz Wellhausen, python package from Qingwen Zhang.

Running on macOS, Windows and Linux, with Python Version >= 3.8.

Available in:

📜 Change Log:

• 2025-04-18: Add __init__.py to the package with ascontiguousarray to avoid unexpected point error reading, add package to the latest python (3.13) also. Rename the default branch to main.
• 2024-07-03: Speed up nanobind np.array <-> std::vector<Eigen:: Vector3d> conversion and also NOMINSIZE in make. Speed difference: 0.1s -> 0.01s. Based on discussion here.
• 2024-02-15: Initial version.

0. Setup

Choose one of the following options to install the package (recommended to use Option A pip install linefit):

Option A: Install from pypi pip install linefit

Option B: Clone this repo and run following to build:

pip install git+https://github.com/Kin-Zhang/linefit
python3 -c 'import linefit; print("linefit ground seg lib import success")'

Dependencies for demo:

# for reading data and visualization
pip install numpy open3d

1. Run the example

Demo usage:

pc_data = np.load("kitti_pc0.npy") # [N, 3]
groundseg = ground_seg("config.toml")
label = np.array(groundseg.run(pc_data[:,:3])) # [N, 1]
# 1: ground, 0: non-ground for this pc_data

You can check the full example script in example.py. If you run the example script, it will directly show a default effect of demo data.

python example.py

A window will pop up and show the ground segmentation result.

Parameter description

Parameters are set in assets/config.toml. I also provided several popular datasets' parameters in assets/config. Note that the origin pose should in sensor link but base link of the robot or vehicle. The process step can be found in DeFlow/SeFlow.

TL;DR: tune the sensor_height to offset the ground point z to 0. Others are optional for better performance. If you are interested in the details, please read the following.

This algorithm works on the assumption that you known the height of the sensor above ground. Therefore, you have to adjust the sensor_height to your robot specifications, otherwise, it will not work.

The default parameters should work on the KITTI dataset.

Ground Condition

• sensor_height Sensor height above ground.
• max_dist_to_line maximum vertical distance of point to line to be considered ground.
• max_slope Maximum slope of a line.
• min_slope Minimum slope of a line.
• max_fit_error Maximum error a point is allowed to have in a line fit.
• max_start_height Maximum height difference between new point and estimated ground height to start a new line.
• long_threshold Distance after which the max_height condition is applied.
• max_height Maximum height difference between line points when they are farther apart than long_threshold.
• line_search_angle How far to search in angular direction to find a line. A higher angle helps fill "holes" in the ground segmentation.

Segmentation

• r_min Distance at which segmentation starts.
• r_max Distance at which segmentation ends.
• n_bins Number of radial bins.
• n_segments Number of angular segments.

Other

• n_threads Number of threads to use.

Acknowledgement & Citation

The original C++ code is from the repo we forked: lorenwel/linefit_ground_segmentation.

The original methods are described in the following paper:

@inproceedings{himmelsbach2010fast,
  title={Fast segmentation of 3d point clouds for ground vehicles},
  author={Himmelsbach, Michael and Hundelshausen, Felix V and Wuensche, H-J},
  booktitle={Intelligent Vehicles Symposium (IV), 2010 IEEE},
  pages={560--565},
  year={2010},
  organization={IEEE}
}

This Python package is developed during HiMo project, please consider to cite our paper if this python package is helpful for your research:

@article{zhang2025himo,
    title={HiMo: High-Speed Objects Motion Compensation in Point Cloud},
    author={Zhang, Qingwen and Khoche, Ajinkya and Yang, Yi and Ling, Li and Sina, Sharif Mansouri and Andersson, Olov and Jensfelt, Patric},
    year={2025},
    journal={arXiv preprint arXiv:2503.00803},
}

More python binding examples can be found in our other project:

• dufomap: a dynamic awareness mapping framework. Remove dynamic points in a raw map.
• dztimer: a breakout timer for python code.