fast_gicp
1.0.0
我们发布了Small_GICP，其速度是FAST_GICP的两倍，并且具有最低依赖性和清洁界面。
该软件包是基于GICP的快速点云注册算法的集合。它构成了我们的Voxelized GICP（VGICP）算法的多线程GICP以及多线程和GPU实现。所有实施的算法都具有PCL注册接口，因此可以用作PCL中GICP的Inploph替换。
我们已经在Ubuntu 18.04/20.04和Cuda 11.1上测试了此软件包。
在MacOS上使用brew时，您可能必须设置这样的依据
cmake .. "-DCMAKE_PREFIX_PATH=$(brew --prefix libomp)[;other-custom-prefixes]" -DQt5_DIR=$(brew --prefix qt@5)lib/cmake/Qt5
要启用CUDA驱动的实现，请将BUILD_VGICP_CUDA cmake选项设置为ON 。
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=ONgit 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 cd fast_gicp
python3 setup.py install --user注意：如果您在启用Catkin的环境上并且安装不正常，请在cmakelists.txt中注释find_package(catkin) ，然后再次运行上述安装命令。
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 ()CPU：Core i9-9900K GPU：GEFORCE RTX2080TI
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
有关详细用法，请参见src/align.cpp。
# Perform frame-by-frame registration
rosrun fast_gicp gicp_kitti /your/kitti/path/sequences/00/velodyne
cd fast_gicp/src
python3 kitti.py /your/kitti/path/sequences/00/velodyne在某些环境中，设置较少的线程数而不是（默认）最大线程数可能会导致处理速度更快（请参见＃145（注释））。
Kenji Koide，[email protected]
日本国家先进工业科学技术研究所以人为本的流动性研究中心[url]
