cuPCL has some libraries used to process points cloud with CUDA and some samples for their usage. There are several subfolders in the project and every subfolder has:
- lib implemented by CUDA
- Sample code showing the lib usage and checking the perf and accuracy by comparing its output with PCL's
To get started, follow the instructions below.
Xavier, Orin, and Linux x86 are supported(For Jetpack 4.x, Jetpack 5.x, and Linux x86_64 library, please check the respective branch).
If you run into any issues please let us know.
To get started, follow these steps.
Install PCL (Eigen included)
$sudo apt-get update
$sudo apt-get install libpcl-dev
Enter any subfolder and then
make
- Please boost CPU and GPU firstly
sudo nvpmodel -m 0
sudo jetson_clocks
- Usage:
./demo [*.pcd]
$ strings lib* | grep version | grep lib
lib* version: 1.0 Jun 2 2019 09:30:19
Jetson Xavier AGX 8GB
Jetpack 4.4.1
CUDA 10.2
PCL 1.8
Eigen 3
This project provides:
- lib for Icp implemented by CUDA
- Sample code showing the lib usage and checking the perf and accuracy by comparing its output with PCL's
- two point clounds: test_P.pcd and test_Q.pcd that both having 7000 points
The project provides:
- lib for Filter implemented by CUDA
- Sample code showing the lib usage and checking the perf and accuracy by comparing its output with PCL's
- A point clound: sample.pcd which has 119978 points
NOTE: Now it supports two kinds of filters: PassThrough and VoxelGrid.
This package provides:
- lib for Segmentation implemented by CUDA
- Sample code showing the lib usage and checking the perf and accuracy by comparing its output with PCL's
- A point clound: sample.pcd which has 119978 points
NOTE: Now it just supports SAC_RANSAC SACMODEL_PLANE.
This package provides:
- lib for Octree implemented by CUDA
- Sample code showing the lib usage and checking the perf and accuracy by comparing its output with PCL's
- A point clound: sample.pcd which has 119978 points
NOTE: Now it just supports Radius Search and Approx Nearest Search
This package provides:
- lib for Cluster implemented by CUDA
- Sample code showing the lib usage and checking the perf and accuracy by comparing its output with PCL's
NOTE:
- Cluster can be used to extract objects from points cloud after road plane was removed by Segmentation.
- The sample will use a PCD(sample.pcd) file which had removed road plane.
This package provides:
- lib for NDT implemented by CUDA
- Sample code showing the lib usage and checking the perf and accuracy by comparing its output with PCL's
- two point clounds: test_P.pcd and test_Q.pcd that both having 7000 points
GPU | CPU-GICP | CPU-ICP | |
---|---|---|---|
count of points cloud | 7000 | 7000 | 7000 |
maximum of iterations | 20 | 20 | 20 |
cost time(ms) | 43.3 | 652.8 | 7746.0 |
fitness_score(the lower the better) | 0.514 | 0.525 | 0.643 |
GPU | CPU | |
---|---|---|
count of points cloud | 11w | 11w |
down,up FilterLimits | (-0.5, 0.5) | (-0.5, 0.5) |
limitsNegative | false | false |
Points selected | 5110 | 5110 |
cost time(ms) | 0.660954 | 2.97487 |
GPU | CPU | |
---|---|---|
count of points cloud | 11w | 11w |
LeafSize | (1,1,1) | (1,1,1) |
Points selected | 3440 | 3440 |
cost time(ms) | 3.12895 | 7.26262 |
GPU | CPU | |
---|---|---|
segment by time(ms) | 14.9346 | 69.6264 |
model coefficients | {-0.00273056, 0.0425288, 0.999092, 1.75528} | {-0.00273045, 0.0425287, 0.999092, 1.75528} |
find points | 9054 | 9054 |
GPU | CPU | |
---|---|---|
count of points cloud | 119978 | 119978 |
down,up FilterLimits | (0.0,1.0) | (0.0,1.0) |
limitsNegative | false | false |
Points selected | 16265 | 16265 |
cost time(ms) | 0.589752 | 2.82811 |
GPU | CPU | |
---|---|---|
Count of points cloud | 17w | 17w |
Cluster cost time(ms) | 10.3122 | 4016.85 |
GPU | CPU | |
---|---|---|
count of points cloud | 7000 | 7000 |
cost time(ms) | 34.7789 | 136.858 |
fitness_score(the lower the better) | 0.538 | 0.540 |
https://developer.nvidia.com/blog/accelerating-lidar-for-robotics-with-cuda-based-pcl/ https://developer.nvidia.com/blog/detecting-objects-in-point-clouds-with-cuda-pointpillars/