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CUDA-based 3D data processing and robotics library

License: MIT License

Cuda 37.06% C++ 44.97% CMake 1.23% C 14.80% Python 1.28% GLSL 0.67%

cupoch's Introduction

CUDA-based 3D Data Processing and Robotics Library

Build Status PyPI version PyPI - Python Version Downloads

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

The goal of this library is to process the input of 3D sensors rapidly and use it to control the robot. This library is based on the functionality of Open3D, with additional features for integration into robot systems.

Core Features

  • 3D data processing and robotics computation using CUDA
    • Point cloud registration
    • Point cloud clustering
    • Point cloud/Triangle mesh filtering, down sampling
    • Visual Odometry
    • Collision checking
    • Occupancy grid
  • Open3D-like API
  • Support memory pool and managed allocators
  • Interactive GUI (OpenGL CUDA interop and imgui)
  • Interoperability between cupoch 3D data and DLPack(Pytorch, Cupy,...) data structure

Installation

This software is tested under 64 Bit Ubuntu Linux 18.04 and CUDA 10.0/10.1/10.2. 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

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

sudo apt-get install libxinerama-dev libxcursor-dev libglu1-mesa-dev
pip3 install https://github.com/neka-nat/cupoch/releases/download/v0.0.8/cupoch-0.0.8.0-cp36-cp36m-linux_aarch64.whl

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

speedup

Visual odometry with intel realsense D435

vo

Occupancy grid with intel realsense D435

og

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 Voxel Grid
Occupancy Grid Image

References

cupoch's People

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