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https://github.com/SMRT-AIST/fast_gicp

A collection of GICP-based fast point cloud registration algorithms
https://github.com/SMRT-AIST/fast_gicp

cpp cuda gicp gpu icp multithreading pcl point-cloud python registration scan-matching vgicp

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A collection of GICP-based fast point cloud registration algorithms

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# fast_gicp



This package is a collection of GICP-based fast point cloud registration algorithms. It constains a multi-threaded GICP as well as multi-thread and GPU implementations of our voxelized GICP (VGICP) algorithm. All the implemented algorithms have the PCL registration interface so that they can be used as an inplace replacement for GICP in PCL.



- FastGICP: multi-threaded GICP algorithm (**\~40FPS**)

- FastGICPSingleThread: GICP algorithm optimized for single-threading (**\~15FPS**)

- FastVGICP: multi-threaded and voxelized GICP algorithm (**\~70FPS**)

- FastVGICPCuda: CUDA-accelerated voxelized GICP algorithm (**\~120FPS**)

- NDTCuda: CUDA-accelerated D2D NDT algorithm (**\~500FPS**)

![proctime](data/proctime.png)



[![Build](https://github.com/SMRT-AIST/fast_gicp/actions/workflows/build.yml/badge.svg)](https://github.com/SMRT-AIST/fast_gicp/actions/workflows/build.yml) on melodic & noetic



## Installation



### Dependencies

- PCL

- Eigen

- OpenMP

- CUDA (optional)

- [Sophus](https://github.com/strasdat/Sophus)

- [nvbio](https://github.com/NVlabs/nvbio)



We have tested this package on Ubuntu 18.04/20.04 and CUDA 11.1.



On macOS when using `brew`, you might have to set up your depenencies like this



```

cmake .. "-DCMAKE_PREFIX_PATH=$(brew --prefix libomp)[;other-custom-prefixes]" -DQt5_DIR=$(brew --prefix qt@5)lib/cmake/Qt5

```



### CUDA



To enable the CUDA-powered implementations, set ```BUILD_VGICP_CUDA``` cmake option to ```ON```.



### ROS

```bash

cd ~/catkin_ws/src

git clone https://github.com/SMRT-AIST/fast_gicp --recursive

cd .. && catkin_make -DCMAKE_BUILD_TYPE=Release

# enable cuda-based implementations

# cd .. && catkin_make -DCMAKE_BUILD_TYPE=Release -DBUILD_VGICP_CUDA=ON

```



### Non-ROS

```bash

git clone https://github.com/SMRT-AIST/fast_gicp --recursive

mkdir fast_gicp/build && cd fast_gicp/build

cmake .. -DCMAKE_BUILD_TYPE=Release

# enable cuda-based implementations

# cmake .. -DCMAKE_BUILD_TYPE=Release -DBUILD_VGICP_CUDA=ON

make -j8

```



### Python bindings

```bash

cd fast_gicp

python3 setup.py install --user

```

Note: If you are on a catkin-enabled environment and the installation doesn't work well, comment out ```find_package(catkin)``` in CMakeLists.txt and run the above installation command again.



```python

import pygicp



target = # Nx3 numpy array

source = # Mx3 numpy array



# 1. function interface

matrix = pygicp.align_points(target, source)



# optional arguments

# initial_guess               : Initial guess of the relative pose (4x4 matrix)

# method                      : GICP, VGICP, VGICP_CUDA, or NDT_CUDA

# downsample_resolution       : Downsampling resolution (used only if positive)

# k_correspondences           : Number of points used for covariance estimation

# max_correspondence_distance : Maximum distance for corresponding point search

# voxel_resolution            : Resolution of voxel-based algorithms

# neighbor_search_method      : DIRECT1, DIRECT7, DIRECT27, or DIRECT_RADIUS

# neighbor_search_radius      : Neighbor voxel search radius (for GPU-based methods)

# num_threads                 : Number of threads



# 2. class interface

# you may want to downsample the input clouds before registration

target = pygicp.downsample(target, 0.25)

source = pygicp.downsample(source, 0.25)



# pygicp.FastGICP has more or less the same interfaces as the C++ version

gicp = pygicp.FastGICP()

gicp.set_input_target(target)

gicp.set_input_source(source)

matrix = gicp.align()



# optional

gicp.set_num_threads(4)

gicp.set_max_correspondence_distance(1.0)

gicp.get_final_transformation()

gicp.get_final_hessian()

```



## Benchmark

CPU:Core i9-9900K GPU:GeForce RTX2080Ti



```bash

roscd fast_gicp/data

rosrun fast_gicp gicp_align 251370668.pcd 251371071.pcd

```



```

target:17249[pts] source:17518[pts]

--- pcl_gicp ---

single:127.508[msec] 100times:12549.4[msec] fitness_score:0.204892

--- pcl_ndt ---

single:53.5904[msec] 100times:5467.16[msec] fitness_score:0.229616

--- fgicp_st ---

single:111.324[msec] 100times:10662.7[msec] 100times_reuse:6794.59[msec] fitness_score:0.204379

--- fgicp_mt ---

single:20.1602[msec] 100times:1585[msec] 100times_reuse:1017.74[msec] fitness_score:0.204412

--- vgicp_st ---

single:112.001[msec] 100times:7959.9[msec] 100times_reuse:4408.22[msec] fitness_score:0.204067

--- vgicp_mt ---

single:18.1106[msec] 100times:1381[msec] 100times_reuse:806.53[msec] fitness_score:0.204067

--- vgicp_cuda (parallel_kdtree) ---

single:15.9587[msec] 100times:1451.85[msec] 100times_reuse:695.48[msec] fitness_score:0.204061

--- vgicp_cuda (gpu_bruteforce) ---

single:53.9113[msec] 100times:3463.5[msec] 100times_reuse:1703.41[msec] fitness_score:0.204049

--- vgicp_cuda (gpu_rbf_kernel) ---

single:5.91508[msec] 100times:590.725[msec] 100times_reuse:226.787[msec] fitness_score:0.20557

```



See [src/align.cpp](https://github.com/SMRT-AIST/fast_gicp/blob/master/src/align.cpp) for the detailed usage.



## Test on KITTI



### C++



```bash

# Perform frame-by-frame registration

rosrun fast_gicp gicp_kitti /your/kitti/path/sequences/00/velodyne

```



![kitti00](https://user-images.githubusercontent.com/31344317/86207074-b98ac280-bba8-11ea-9687-e65f03aaf25b.png)



### Python



```bash

cd fast_gicp/src

python3 kitti.py /your/kitti/path/sequences/00/velodyne

```



## Note



In some environments, setting a fewer number of threads rather than the (default) maximum number of threads may result in faster processing (see https://github.com/SMRT-AIST/fast_gicp/issues/145#issuecomment-1890885373).



## Related packages

- [ndt_omp](https://github.com/koide3/ndt_omp)

- [fast_gicp](https://github.com/SMRT-AIST/fast_gicp)



## Papers

- Kenji Koide, Masashi Yokozuka, Shuji Oishi, and Atsuhiko Banno, Voxelized GICP for fast and accurate 3D point cloud registration, ICRA2021 [[link]](https://easychair.org/publications/preprint/ftvV)



## Contact

Kenji Koide, [email protected]



Human-Centered Mobility Research Center, National Institute of Advanced Industrial Science and Technology, Japan  [\[URL\]](https://unit.aist.go.jp/rirc/en/team/smart_mobility.html)