https://github.com/yzrobot/adaptive_clustering
[ROS package] Lightweight and Accurate Point Cloud Clustering
https://github.com/yzrobot/adaptive_clustering
clustering point-cloud point-cloud-segmentation ros velodyne
Last synced: 7 months ago
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[ROS package] Lightweight and Accurate Point Cloud Clustering
- Host: GitHub
- URL: https://github.com/yzrobot/adaptive_clustering
- Owner: yzrobot
- License: bsd-3-clause
- Created: 2019-02-25T10:20:02.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2023-07-14T12:37:16.000Z (about 2 years ago)
- Last Synced: 2024-10-27T20:18:47.016Z (12 months ago)
- Topics: clustering, point-cloud, point-cloud-segmentation, ros, velodyne
- Language: C++
- Homepage:
- Size: 266 KB
- Stars: 374
- Watchers: 9
- Forks: 93
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Adaptive Clustering: A lightweight and accurate point cloud clustering method #
[](https://travis-ci.org/yzrobot/adaptive_clustering)
[](https://www.codacy.com/app/yzrobot/adaptive_clustering?utm_source=github.com&utm_medium=referral&utm_content=yzrobot/adaptive_clustering&utm_campaign=Badge_Grade)
[](https://opensource.org/licenses/BSD-3-Clause)[](https://www.youtube.com/watch?v=rmPn7mWssto)
## Changelog ##
* **\[Apr 14, 2022\]:** Two new branches, [gpu](https://github.com/yzrobot/adaptive_clustering/tree/gpu) and [agx](https://github.com/yzrobot/adaptive_clustering/tree/agx), have been created for GPU-based implementations:
* [gpu](https://github.com/yzrobot/adaptive_clustering/tree/gpu) is based on [PCL-GPU](https://pcl.readthedocs.io/projects/tutorials/en/master/#gpu) and has been tested with an NVIDIA TITAN Xp.
* [agx](https://github.com/yzrobot/adaptive_clustering/tree/agx) is based on [CUDA-PCL](https://github.com/NVIDIA-AI-IOT/cuda-pcl) and has been tested with an NVIDIA Jetson AGX Xavier.* **\[Feb 25, 2019\]:** A new branch, [devel](https://github.com/yzrobot/adaptive_clustering/tree/devel), faster (by downsampling) and better (by merging clusters split by nested regions and on the z-axis).
## How to build ##
```sh
cd ~/catkin_ws/src/
git clone https://github.com/yzrobot/adaptive_clustering.git
cd ~/catkin_ws
catkin_make
```## Citation ##
If you are considering using this code, please reference the following:
```
@article{yz19auro,
author = {Zhi Yan and Tom Duckett and Nicola Bellotto},
title = {Online learning for 3D LiDAR-based human detection: Experimental analysis of point cloud clustering and classification methods},
journal = {Autonomous Robots},
year = {2019}
}
```