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https://github.com/hku-mars/ikd-Tree
This repository provides implementation of an incremental k-d tree for robotic applications.
https://github.com/hku-mars/ikd-Tree
kd-tree lidar-mapping lidar-point-cloud
Last synced: 3 months ago
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This repository provides implementation of an incremental k-d tree for robotic applications.
- Host: GitHub
- URL: https://github.com/hku-mars/ikd-Tree
- Owner: hku-mars
- License: gpl-2.0
- Created: 2021-02-22T06:07:06.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2022-11-21T08:28:07.000Z (about 2 years ago)
- Last Synced: 2024-08-01T03:22:50.457Z (6 months ago)
- Topics: kd-tree, lidar-mapping, lidar-point-cloud
- Language: C++
- Homepage:
- Size: 89 MB
- Stars: 600
- Watchers: 9
- Forks: 170
- Open Issues: 13
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# ikd-Tree
**ikd-Tree** is an incremental k-d tree designed for robotic applications. The ikd-Tree incrementally updates a k-d tree with new coming points only, leading to much lower computation time than existing static k-d trees. Besides point-wise operations, the ikd-Tree supports several features such as box-wise operations and down-sampling that are practically useful in robotic applications.
## What does ikd-Tree support?
- Build a balanced k-d tree - `Build()`
- Dynamically insert points to or delete points from the k-d tree - `Add_Points() / Delete_Points()`
- Delete points inside given axis-aligned bounding boxes - `Delete_Point_Boxes()`
- K Nearest Neighbor Search with range limitation - `Nearest_Search()`
- Acquire points inside a given axis-aligned bounding box on the k-d tree - `Box_Search()`
- Acquire points inside a ball with given radius on the k-d tree - `Radius_Search()`
## User Manual
- Browse the [User Manual](https://github.com/hku-mars/ikd-Tree/blob/main/documents/UserManual.pdf) for using our ikd-Tree.
## Developers
- [Yixi CAI 蔡逸熙](https://github.com/Ecstasy-EC): Data structure design and implementation
- [Wei XU 徐威](https://github.com/XW-HKU): Incorporation into [LiDAR-inertial odometry package FAST_LIO2 (TRO, 2022)](https://github.com/hku-mars/FAST_LIO)
## Related paper
- [ikd-Tree: An Incremental K-D Tree for robotic applications](https://arxiv.org/abs/2102.10808)
- [FAST-LIO2: Fast Direct LiDAR-Inertial Odometry](https://ieeexplore.ieee.org/abstract/document/9697912)
If you are using any code of this repo in your research, please cite at least one of the articles as following:
- **ikd-Tree**
```
@article{cai2021ikd,
title={ikd-Tree: An Incremental KD Tree for Robotic Applications},
author={Cai, Yixi and Xu, Wei and Zhang, Fu},
journal={arXiv preprint arXiv:2102.10808},
year={2021}
}
```
- **FAST-LIO2**
```
@article{xu2022fast,
title={Fast-lio2: Fast direct lidar-inertial odometry},
author={Xu, Wei and Cai, Yixi and He, Dongjiao and Lin, Jiarong and Zhang, Fu},
journal={IEEE Transactions on Robotics},
year={2022},
publisher={IEEE}
}
```
## Build & Run demo
### 1. How to build this project
```bash
cd ~/catkin_ws/src
git clone [email protected]:hku-mars/ikd-Tree.git
cd ikd-Tree/build
cmake ..
make -j 9
```
### 2. Run our examples
**Note: To run Example 2 & 3, please download the PCD file ([HKU_demo_pointcloud](https://drive.google.com/file/d/1tMYiBIFn-fcjisaoIrmIKA09NICGG9KJ/view?usp=sharing)) into`${Your own directory}/ikd-Tree/materials`**
```bash
cd ${Your own directory}/ikd-Tree/build
# Example 1. Check the speed of ikd-Tree
./ikd_tree_demo
# Example 2. Searching-points-by-box examples
./ikd_Tree_Search_demo
# Example 3. An aysnc. exmaple for readers' better understanding of the principle of ikd-Tree
./ikd_tree_async_demo
```
**Example 2: ikd_tree_Search_demo**
Box Search Result | Radius Search Result
:-------------------------:|:-------------------------:
 | 
Points returned from the two search methods are shown in red.
**Example 3: ikd_tree_Async_demo**
Original Map:
Box Delete Results:
Points removed from ikd-Tree(red) | Map after box delete
:-------------------------:|:-------------------------:
 | 
This example is to demonstrate the asynchronous phenomenon in ikd-Tree. The points are deleted by attaching 'deleted' on the tree nodes (map shown in the ) instead of being removed from the ikd-Tree immediately. They are removed from the tree when rebuilding process is performed. Please refer to our paper for more details about delete and rebuilding.
## Acknowledgments
- Thanks [Marcus Davi](https://github.com/Marcus-Davi) for helps in templating the ikd-Tree for more general applications.
- Thanks [Hyungtae Lim 임형태](https://github.com/LimHyungTae) for providing application examples on point clouds.
## License
The source code of ikd-Tree is released under [GPLv2](http://www.gnu.org/licenses/old-licenses/gpl-2.0.html) license. For commercial use, please contact Mr. Yixi CAI () or Dr. Fu ZHANG ().