https://github.com/limhyungtae/openflann
Performance comparison of NearestSearch and radiusSearch function in point cloud.
https://github.com/limhyungtae/openflann
nanoflann pcl-flann robotics-applications
Last synced: 3 months ago
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Performance comparison of NearestSearch and radiusSearch function in point cloud.
- Host: GitHub
- URL: https://github.com/limhyungtae/openflann
- Owner: LimHyungTae
- Created: 2021-04-15T15:08:08.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2021-04-30T06:44:09.000Z (over 4 years ago)
- Last Synced: 2023-03-03T22:32:12.874Z (over 2 years ago)
- Topics: nanoflann, pcl-flann, robotics-applications
- Language: C++
- Homepage:
- Size: 1.62 MB
- Stars: 7
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# OpenFLANN
I found PicoFLANN and compare the speed of alorithms, namely *PCL FLANN*, *NanoFLANN*, and *PicoFLANN*.
## Characteristics
* Set NanoFLANN and PicoFLANN to be avaiable on Point Cloud Libarary (PCL).
* It's simple to use them! Just git pull this repository, then copy & paste the header files in `include` folder.
* Usage is totally same with Point Cloud Library, i.e., `setInputCloud`, `nearestSearch`, or `radiusSearch`.
* Many robotics guys refer to [NanoFLANN in LOAM](https://github.com/laboshinl/loam_velodyne/blob/master/include/loam_velodyne/nanoflann_pcl.h); however, the function `radiusSearch` in LOAM is actually not in use so the function does not work properly. So, I debugged it and revise the `radiusSearch`.
## Reference
* [PCL FLANN](https://pointclouds.org/documentation/tutorials/kdtree_search.html)
* [NanoFLANN](https://github.com/jlblancoc/nanoflann)
* [PicoFLANN](https://github.com/rmsalinas/picoflann) (The original developer says it is faster than NanoFLANN, and it actually is when the scale of points becomes larger)
## Simulation results
Experiments: Please refer to `src/main.cpp`. I reran the experiment 1,000 times to measure mean speed of each case.
One-line summary: For robotics application, NanoFLANN is better than other FLANN implementations.
You can show the results via python code (tabulate is necessary)
```
cd outputs
python viz_output.py
```
### K-Nearest Neighbor
### Radius Search
