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# small_gicp (fast_gicp2)
**small_gicp** is a header-only C++ library that provides efficient and parallelized fine point cloud registration algorithms (ICP, Point-to-Plane ICP, GICP, VGICP, etc.). It is a refined and optimized version of its predecessor, [fast_gicp](https://github.com/SMRT-AIST/fast_gicp), with the following features.
**small_gicp** is a header-only C++ library that provides efficient and parallelized fine point cloud registration algorithms (ICP, Point-to-Plane ICP, GICP, VGICP, etc.). It is a refined and optimized version of its predecessor, [fast_gicp](https://github.com/SMRT-AIST/fast_gicp), with the following features. Note that GPU-based implementations are NOT included in this package.
- **Highly ptimized** : The implementation of the core registration algorithm is further optimized from that in fast_gicp. It offers up to 2x speed up compared to fast_gicp.
- **All parallerized** : small_gicp offers parallelized implementations of several algorithms in the point cloud registration process (Downsampling, KdTree construction, Normal/covariance estimation). As a parallelism backend, either (or both) of [OpenMP](https://www.openmp.org/) and [Intel TBB](https://github.com/oneapi-src/oneTBB) can be used.
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### Downsampling
- Single-threaded `small_gicp::voxelgrid_sampling` is about 1.3x faster than `pcl::VoxelGrid`.
- Multi-threaded `small_gicp::voxelgrid_sampling_tbb` (6 threads) is about 3.2x faster than `pcl::VoxelGrid`.
- Single-threaded `small_gicp::voxelgrid_sampling` is about **1.3x faster** than `pcl::VoxelGrid`.
- Multi-threaded `small_gicp::voxelgrid_sampling_tbb` (6 threads) is about **3.2x faster** than `pcl::VoxelGrid`.
- `small_gicp::voxelgrid_sampling` gives accurate downsampling results (almost identical to those of `pcl::VoxelGrid`) while `pcl::ApproximateVoxelGrid` yields spurious points (up to 2x points).
- `small_gicp::voxelgrid_sampling` can process a larger point cloud with a fine voxel resolution compared to `pcl::VoxelGrid` (for a point cloud of 150m width, minimum voxel resolution can be 0.07 mm).
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### KdTree construction
- Multi-threaded implementation (TBB and OMP) can be up to 4x faster than the single-threaded one (All the implementations are based on nanoflann).
- Multi-threaded implementation (TBB and OMP) can be up to **4x faster** than the single-threaded one (All the implementations are based on nanoflann).
- Basically the processing speed get faster as the number of threads increases, but the speed gain is not monotonic sometimes (because of the scheduling algorithm or some CPU(AMD 5995WX)-specific issues?).
- This benchmark only compares the construction time (query time is not included).
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### Odometry estimation
- Single-thread `small_gicp::GICP` is about 2.4x and 1.9x faster than `pcl::GICP` and `fast_gicp::GICP`, respectively.
- Single-thread `small_gicp::GICP` is about **2.4x and 1.9x faster** than `pcl::GICP` and `fast_gicp::GICP`, respectively.
- `small_gicp::(GICP|VGICP)` shows a better multi-thread scalability compared to `fast_gicp::(GICP|VGICP)`.
- `small_gicp::GICP` parallelized with [TBB flow graph](https://github.com/koide3/small_gicp/blob/master/src/odometry_benchmark_small_gicp_tbb_flow.cpp) shows an excellent scalablity to many-threads situations (~128 threads) but with a latency degradation.
- `small_gicp::GICP` parallelized with [TBB flow graph](https://github.com/koide3/small_gicp/blob/master/src/odometry_benchmark_small_gicp_tbb_flow.cpp) shows an excellent scalablity to many-threads situations (**~128 threads**) but with latency degradation.
![odometry_time](docs/assets/odometry_time.png)
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If you find this package useful for your project, please consider leaving a comment here. It would help the author gain internal recognition in his organization and keep working on this project.
## Papers
- Kenji Koide, Masashi Yokozuka, Shuji Oishi, and Atsuhiko Banno, Voxelized GICP for Fast and Accurate 3D Point Cloud Registration, ICRA2021
## Contact
[Kenji Koide](https://staff.aist.go.jp/k.koide/), National Institute of Advanced Industrial Science and Technology (AIST)