Skip to content

neka-nat/cupoch

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Robotics with GPU computing

Build status Build statusPyPI version PyPI - Python Version Downloads xscode

Buy Me A Coffee

Cupoch is a library that implements rapid 3D data processing for robotics using CUDA.

The goal of this library is to implement fast 3D data computation in robot systems. For example, it has applications in SLAM, collision avoidance, path planning and tracking. This repository is based on Open3D.

Core Features

Installation

This library is packaged under 64 Bit Ubuntu Linux 20.04 and CUDA 11.7. You can install cupoch using pip.

pip install cupoch

Or install cupoch from source.

git clone https://github.com/neka-nat/cupoch.git --recurse
cd cupoch
mkdir build
cd build
cmake ..; make install-pip-package -j

Installation for Jetson Nano

You can also install cupoch using pip on Jetson Nano. Please set up Jetson using jetpack and install some packages with apt.

sudo apt-get install xorg-dev libxinerama-dev libxcursor-dev libglu1-mesa-dev
pip3 install cupoch

Or you can compile it from source. Update your version of cmake if necessary.

wget https://github.com/Kitware/CMake/releases/download/v3.18.4/cmake-3.18.4.tar.gz
tar zxvf cmake-3.18.4.tar.gz
cd cmake-3.18.4
./bootstrap -- -DCMAKE_USE_OPENSSL=OFF
make && sudo make install
cd ..
git clone -b jetson_nano https://github.com/neka-nat/cupoch.git --recurse
cd cupoch/
mkdir build
cd build/
export PATH=/usr/local/cuda/bin:$PATH
cmake -DBUILD_GLEW=ON -DBUILD_GLFW=ON -DBUILD_PNG=ON -DBUILD_JSONCPP=ON ..
sudo make install-pip-package

Use Docker

docker compose up -d
# xhost +
docker exec -it cupoch bash

Getting Started

Please see how to use cupoch in Getting Started first.

Results

The figure shows Cupoch's point cloud algorithms speedup over Open3D. The environment tested on has the following specs:

  • Intel Core i7-7700HQ CPU
  • Nvidia GTX1070 GPU
  • OMP_NUM_THREAD=1

You can get the result by running the example script in your environment.

cd examples/python/basic
python benchmarks.py

If you get the following error when executing an example that includes 3D drawing, please start the program as follows.

$ cd examples/basic
$ python visualization.py
Load a ply point cloud, print it, and render it
MESA: warning: Driver does not support the 0xa7a0 PCI ID.
libGL error: failed to create dri screen
libGL error: failed to load driver: iris
MESA: warning: Driver does not support the 0xa7a0 PCI ID.
libGL error: failed to create dri screen
libGL error: failed to load driver: iris
Error: unknown error	phong_shader.cu:330
__NV_PRIME_RENDER_OFFLOAD=1 __GLX_VENDOR_LIBRARY_NAME=nvidia python visualization.py

speedup

Visual odometry with intel realsense D435

vo

Occupancy grid with intel realsense D435

og

Kinect fusion with intel realsense L515

kf

Stereo matching

sm

Fast Global Registration

fgr

Point cloud from laser scan

fgr

Collision detection for 2 voxel grids

col

Drone Path planning

dp

Visual odometry with ROS + D435

This demo works in the following environment.

  • ROS melodic
  • Python2.7
# Launch roscore and rviz in the other terminals.
cd examples/python/ros
python realsense_rgbd_odometry_node.py

vo

Visualization

Point Cloud Triangle Mesh Kinematics
Voxel Grid Occupancy Grid Distance Transform
Graph Image

References

Citing

@misc{cupoch,
   author = {Kenta Tanaka},
   year = {2020},
   note = {https://github.com/neka-nat/cupoch},
   title = {cupoch -- Robotics with GPU computing}
}