travel-seg 1.1.0

Python bindings for TRAVEL: traversable ground and above-ground object segmentation for 3D LiDAR scans

travel-seg

Python bindings for TRAVEL — traversable ground and above-ground object segmentation for 3D LiDAR scans.

Install

From PyPI (recommended)

pip install travel-seg

Prebuilt wheels are published for Linux x86_64 (manylinux_2_17), macOS arm64 + x86_64, and Windows x86_64 for CPython 3.8–3.13. As of v1.1, the only system dependency is Eigen — no PCL / Boost install required.

From source (development)

# System deps (Ubuntu)
sudo apt install build-essential cmake libeigen3-dev

# System deps (macOS)
brew install cmake eigen

# System deps (Windows)
vcpkg install eigen3:x64-windows
# then: -DCMAKE_PREFIX_PATH="C:/vcpkg/installed/x64-windows" forwarded by
# the release workflow / pip env on Windows.

git clone https://github.com/url-kaist/TRAVEL.git
cd TRAVEL
pip install -e python/

Usage

import numpy as np
import travel_seg as ts

# KITTI .bin → (N, 4) float32 XYZI
points = np.fromfile("0000.bin", dtype=np.float32).reshape(-1, 4)

# One-shot
result = ts.segment(points)
ground_pts = points[result.ground_mask]
for cid in np.unique(result.instance_labels):
    if cid == 0:
        continue
    cluster_pts = points[result.instance_labels == cid]
    print(f"cluster {cid}: {len(cluster_pts)} points")

# Per-frame loops: build the segmenters once, reuse
tgs = ts.TravelGroundSeg(ts.GroundSegConfig(max_range=80.0))
aos = ts.ObjectCluster(ts.ObjectClusterConfig(vert_scan=64, horz_scan=4500))
for frame in frames:
    ground_mask, elapsed = tgs.estimate_ground(frame[:, :3])
    labels = aos.segment_objects(frame[~ground_mask, :3])
    ...

API

Symbol	Purpose
GroundSegConfig	dataclass — TGS parameters; KITTI-tuned defaults
ObjectClusterConfig	dataclass — AOS parameters; KITTI-tuned defaults
TravelGroundSeg(cfg)	reusable ground segmenter; .estimate_ground(points) -> (mask, elapsed)
ObjectCluster(cfg)	reusable object clusterer; .segment_objects(points) -> labels
segment(points, ground_config=None, object_config=None)	one-shot helper returning a SegmentResult
SegmentResult	dataclass: .ground_mask, .instance_labels, .elapsed_seconds

Inputs are float32 numpy arrays of shape (N, 3) (XYZ) or (N, 4) (XYZI). The intensity column is currently ignored by both TGS and AOS but accepted for convenience when reading raw KITTI bins.

Outputs are length-N numpy arrays aligned with the original input order:

• ground_mask: bool, True where the input point was classified as ground.
• instance_labels: int32. 0 = ground / unclassified / dropped. 1..K = per-cluster ids within this single frame (not consistent across frames).

Tests / examples

pip install -e python/[test]
pytest python/tests/

# Real data demo
python python/examples/run_kitti.py /path/to/kitti/sequences/00 0 /tmp/out

Citation

See the top-level README.

Release process

Publishing uses PyPI Trusted Publishing (OIDC), so there are no API tokens or GitHub secrets to manage. PyPI authenticates the GitHub Actions workflow directly based on a one-time pending-publisher registration.

One-time PyPI setup

Visit https://pypi.org/manage/account/publishing/ and add a pending publisher with these values (only required before the very first release; it auto-graduates to a regular trusted publisher after the first upload):

Field	Value
PyPI Project Name	travel-seg
Owner	url-kaist
Repository name	TRAVEL
Workflow name	release.yml
Environment name	pypi

GitHub side: create an environment named pypi at Settings -> Environments (no secrets/protection rules needed unless you want manual approval).

Per-release flow

1. Bump version in three places to keep them in sync:
   • python/pyproject.toml -> [project] version
   • python/travel_seg/__init__.py -> __version__
   • python/travel_seg/pybind/travel_pybind.cpp -> m.attr("__version__")
2. Commit, tag, push:

   git commit -am "release: travel-seg vX.Y.Z"
   git tag vX.Y.Z
   git push origin main vX.Y.Z
   

3. Trigger the Release (PyPI) workflow from the GitHub Actions UI.
4. Verify the install in a fresh venv:

   python -m venv /tmp/check && source /tmp/check/bin/activate
   pip install travel-seg==X.Y.Z
   python -c "import travel_seg as ts; import numpy as np; \
              ts.segment(np.random.uniform(-10,10,(2000,3)).astype(np.float32))"