cuPCL
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.
Getting Started
To get started, follow these steps.
Step 1 - Install Dependencies
Install PCL (Eigen included)
$sudo apt-get update
$sudo apt-get install libpcl-dev
Step 2 - Build
Enter any subfolder and then
make
Step 3 - Run
- Please boost CPU and GPU firstly
sudo nvpmodel -m 0
sudo jetson_clocks
- Usage:
./demo [*.pcd]
How to check the Version of the Lib
$ strings lib* | grep version | grep lib
lib* version: 1.0 Jun 2 2019 09:30:19
Test Enviroment
Jetson Xavier AGX 8GB
Jetpack 4.4.1
CUDA 10.2
PCL 1.8
Eigen 3
Functions List
cuICP
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
cuFilter
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.
cuSegmentation
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.
cuOctree
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
cuCluster
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.
cuNDT
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
Performance Comparison
cuICP
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 |
cuFilter
Pass Through
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 |
VoxelGrid
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 |
cuSegmentation
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 |
cuOctree
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 |
cuCluster
GPU | CPU | |
---|---|---|
Count of points cloud | 17w+ | 17w+ |
Cluster cost time(ms) | 10.3122 | 4016.85 |
cuNDT
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 |
Official Blog
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/