CUDA-PCLのJetson AGX Xavierでの動作確認

用意するもの

• Jetson AGX Xavier(JetPack4.5.1)

必要なパッケージのインストール

sudo apt-get update
sudo apt-get install libpcl-dev

動作モードの変更

sudo nvpmodel -m 0

プロジェクトをgit clone

git clone https://github.com/NVIDIA-AI-IOT/cuda-pcl

Make(Segmentation)と実行

cd cuda-pcl/cuda-segmentation/
make

./demo sample.pcd

GPU has cuda devices: 1
----device id: 0 info----
  GPU : Xavier 
  Capbility: 7.2
  Global memory: 15816MB
  Const memory: 64KB
  SM in a block: 48KB
  warp size: 32
  threads in a block: 1024
  block dim: (1024,1024,64)
  grid dim: (2147483647,65535,65535)

-------------------------
CUDA segment by Time: 18.8978 ms.
CUDA modelCoefficients: -0.00269913 0.0424975 0.999093 2.10639
CUDA find points: 7519
-------------------------
PCL(CPU) segment by Time: 80.8026 ms.
Model coefficients: -0.0026991 0.0424981 0.999093 2.10639
Model inliers: 7519

Make(fillter)と実行

cd cuda-pcl/cuda-fillter/
make

./demo sample.pcd

GPU has cuda devices: 1
----device id: 0 info----
  GPU : Xavier 
  Capbility: 7.2
  Global memory: 15816MB
  Const memory: 64KB
  SM in a block: 48KB
  warp size: 32
  threads in a block: 1024
  block dim: (1024,1024,64)
  grid dim: (2147483647,65535,65535)


------------checking CUDA ---------------- 
CUDA Loaded 119978 data points from PCD file with the following fields: x y z

------------checking CUDA PassThrough ---------------- 
CUDA PassThrough by Time: 0.513827 ms.
CUDA PassThrough before filtering: 119978
CUDA PassThrough after filtering: 5110

------------checking CUDA VoxelGrid---------------- 
CUDA VoxelGrid by Time: 5.12201 ms.
CUDA VoxelGrid before filtering: 119978
CUDA VoxelGrid after filtering: 3440


------------checking PCL ---------------- 
PCL(CPU) Loaded 119978 data points from PCD file with the following fields: x y z

------------checking PCL(CPU) PassThrough ---------------- 
PCL(CPU) PassThrough by Time: 4.53807 ms.
PointCloud before filtering: 119978 data points (x y z).
PointCloud after filtering: 5110 data points (x y z).

------------checking PCL VoxelGrid---------------- 
PCL VoxelGrid by Time: 13.6458 ms.
PointCloud before filtering: 119978 data points (x y z).
PointCloud after filtering: 3440 data points (x y z).

Make(ICP)と実行

cd cuda-pcl/cuda-icp/
make

./demo test_P.pcd test_Q.pcd

GPU has cuda devices: 1
----device id: 0 info----
  GPU : Xavier 
  Capbility: 7.2
  Global memory: 15816MB
  Const memory: 64KB
  SM in a block: 48KB
  warp size: 32
  threads in a block: 1024
  block dim: (1024,1024,64)
  grid dim: (2147483647,65535,65535)

Loaded 7000 data points for P with the following fields: x y z
Loaded 7000 data points for Q with the following fields: x y z
 iter.Maxiterate 0
 iter.threshold 0
 iter.acceptrate 1

Target rigid transformation : cloud_in -> cloud_icp
Rotation matrix :
    | 0.923880 -0.382683 0.000000 | 
R = | 0.382683 0.923880 0.000000 | 
    | 0.000000 0.000000 1.000000 | 
Translation vector :
t = < 0.000000, 0.000000, 0.200000 >

------------checking CUDA ICP(GPU)---------------- 
CUDA ICP by Time: 0.671692 ms.
CUDA ICP fitness_score: 0.777453
matrix_icp calculated Matrix by Class ICP 
Rotation matrix :
    | 1.000000 0.000000 -0.000000 | 
R = | -0.000000 1.000000 0.000000 | 
    | -0.000000 0.000000 1.000000 | 
Translation vector :
t = < -0.000000, 0.000000, -0.000000 >

------------checking PCL ICP(CPU)---------------- 
PCL icp.align Time: 36.1408 ms.
has converged: 1 score: 0.651369
CUDA ICP fitness_score: 0.651369
transformation_matrix:
   0.999905  0.00279406   0.0134922   0.0161865
-0.00265722    0.999945   -0.010151  0.00527596
 -0.0135198   0.0101141    0.999858   0.0133578
          0           0           0           1

------------checking PCL GICP(CPU)---------------- 
PCL Gicp.align Time: 143.535 ms.
has converged: 1 score: 0.541552
transformation_matrix:
    0.99874  0.00468762   0.0499603  -0.0427716
-0.00344507    0.999683  -0.0249281   0.0265501
 -0.0500613   0.0247246     0.99844    0.148036
          0           0           0           1