SMRT-AIST / Fast_gicp
Licence: bsd-3-clause
A collection of GICP-based fast point cloud registration algorithms
Stars: ✭ 307
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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)
on melodic & noetic
Installation
Dependencies
We have tested this package on Ubuntu 18.04/20.04 and CUDA 11.1.
CUDA
To enable the CUDA-powered implementations, set BUILD_VGICP_CUDA cmake option to ON.
ROS
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
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
Benchmark
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
See src/align.cpp for the detailed usage.
Test on KITTI
# Perform frame-by-frame registration
rosrun fast_gicp gicp_kitti /your/kitti/path/sequences/00/velodyne
Related packages
Papers
- Kenji Koide, Masashi Yokozuka, Shuji Oishi, and Atsuhiko Banno, Voxelized GICP for fast and accurate 3D point cloud registration, ICRA2021 [link]
Contact
Kenji Koide, [email protected]
Human-Centered Mobility Research Center, National Institute of Advanced Industrial Science and Technology, Japan [URL]
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