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pyTorch wrapper for NVIDIA nvBlox

This package connects nvblox's mapping and collision query functions with pytorch. This package supports both mapping a world from a depth camera and also querying the built world for signed distances. Read nvblox for more information on how it works.

Checkout CuRobo for examples of integrating this package with motion generation for manipulators.

For business inquiries, please visit our website and submit the form: NVIDIA Research Licensing

Updates

• December 2023: Updated wrappers to work with nvblox 0.0.5 and added instructions to compile with PRE_CXX11_ABI.

Citation

If you found this work useful, please cite the below report,

@article{curobo_report23,
         title={CuRobo: Parallelized Collision-Free Minimum-Jerk Robot Motion Generation},
         author={Sundaralingam, Balakumar and Hari, Siva Kumar Sastry and 
         Fishman, Adam and Garrett, Caelan and Van Wyk, Karl and Blukis, Valts and 
         Millane, Alexander and Oleynikova, Helen and Handa, Ankur and 
         Ramos, Fabio and Ratliff, Nathan and Fox, Dieter},
         journal={arXiv preprint},
         year={2023}
        }

Code Contributors

• Valts Blukis
• Balakumar Sundaralingam
• Alexander Millane

Docker

We have found docker to be the most stable way to use nvblox_torch. Docker instructions are at docker_development. The dockerfile is in curobo_github. There are instructions in the link to use nvblox_torch on NVIDIA Jetson and also with NVIDIA Isaac Sim.

Install Instructions

pyTorch that is available through pip wheels and also with Isaac Sim has been compiled with D_GLIBCXX_USE_CXX11_ABI=0. pyTorch that's available through docker containers at ngc are compiled with D_GLIBCXX_USE_CXX11_ABI=1. You can check what value was used for your pytorch installation with python -c "import torch; print(torch._C._GLIBCXX_USE_CXX11_ABI)".

Prequisites:

If you are on Ubuntu older than 20.04:

sudo add-apt-repository ppa:ubuntu-toolchain-r/test
sudo apt update
sudo apt install gcc-9 g++-9
export CC=/usr/bin/gcc-9
export CXX=/usr/bin/g++-9

Installation for CXX11_ABI

1. Install dependenices

   sudo apt-get install libgoogle-glog-dev libgtest-dev libsqlite3-dev curl tcl libbenchmark-dev
   

2. Install nvblox

   git clone https://github.com/valtsblukis/nvblox.git && cd nvblox/nvblox && mkdir build && \
   cmake .. \
   -DPRE_CXX11_ABI_LINKABLE=ON -DBUILD_TESTING=OFF \
   && make -j32 && \
   sudo make install
   

3. Install this repository

   git clone https://github.com/NVlabs/nvblox_torch.git && cd nvblox_torch
   sh install.sh $(python -c 'import torch.utils; print(torch.utils.cmake_prefix_path)')
   python -m pip install -e .
   

Installation for PRECXX11_ABI

The below instructions can also be used to install nvblox torch with Isaac Sim. Change all instances of python to omni_python, where omni_python maps to the python shell of your Isaac Sim installation as alias omni_python='~/.local/share/ov/pkg/isaac_sim-2023.1.0/python.sh'.

1. Create environment variable that stores the value of CXX11_ABI of pytorch installation:

   export TORCH_CXX11=0 # change this value (0=False, 1=True) based on python -c "import torch; print(torch._C._GLIBCXX_USE_CXX11_ABI)"
   

2. Create environment variables that will store the path you want to install nvblox and also the value of CXX11_ABI:

   export PKGS_PATH=/home/${USER}/pkgs
   mkdir -p ${PKGS_PATH}
   

3. Update cmake with:

   cd ${PKGS_PATH} && wget https://cmake.org/files/v3.27/cmake-3.27.1.tar.gz && \
       tar -xvzf cmake-3.27.1.tar.gz && \
       sudo apt update &&  sudo apt install -y build-essential checkinstall zlib1g-dev libssl-dev && \
       cd cmake-3.27.1 && ./bootstrap && \
       make -j8 && \
       sudo make install
   

4. Install sqlite3:

   cd ${PKGS_PATH} && git clone https://github.com/sqlite/sqlite.git -b version-3.39.4 && \
       cd ${PKGS_PATH}/sqlite && CFLAGS=-fPIC ./configure --prefix=${PKGS_PATH}/sqlite/install/ && \
       make -j8 && make install
   

5. Install glog:

   cd ${PKGS_PATH} && git clone https://github.com/google/glog.git -b v0.6.0 && \
   cd glog && \
   mkdir build && cd build && \
   cmake .. -DCMAKE_POSITION_INDEPENDENT_CODE=ON \
   -DCMAKE_INSTALL_PREFIX=${PKGS_PATH}/glog/install/ \
   -DWITH_GFLAGS=OFF -DWITH_GTEST=OFF -DBUILD_SHARED_LIBS=OFF -DCMAKE_CXX_FLAGS=-D_GLIBCXX_USE_CXX11_ABI=${TORCH_CXX11} \
   && make -j8 && make install
   

6. Install gflags:

   cd ${PKGS_PATH} && git clone https://github.com/gflags/gflags.git -b v2.2.2 && \
   cd gflags &&  \
   mkdir build && cd build && \
   cmake .. -DCMAKE_POSITION_INDEPENDENT_CODE=ON \
   -DCMAKE_INSTALL_PREFIX=${PKGS_PATH}/gflags/install/ \
   -DGFLAGS_BUILD_STATIC_LIBS=ON -DCMAKE_CXX_FLAGS=-D_GLIBCXX_USE_CXX11_ABI=${TORCH_CXX11} \
   && make -j8 && make install
   

7. Install nvblox:

   cd ${PKGS_PATH} &&  git clone https://github.com/valtsblukis/nvblox.git && cd ${PKGS_PATH}/nvblox/nvblox mkdir build && cd build && \
   cmake ..  -DBUILD_REDISTRIBUTABLE=ON \
   -DCMAKE_CXX_FLAGS=-D_GLIBCXX_USE_CXX11_ABI=0  -DPRE_CXX11_ABI_LINKABLE=ON \
   -DSQLITE3_BASE_PATH="${PKGS_PATH}/sqlite/install/" -DGLOG_BASE_PATH="${PKGS_PATH}/glog/install/" \
   -DGFLAGS_BASE_PATH="${PKGS_PATH}/gflags/install/" -DCMAKE_CUDA_FLAGS=-D_GLIBCXX_USE_CXX11_ABI=0 && \
   make -j32 && \
   sudo make install
   

8. Install nvblox_torch in your python environment:

   cd ${PKGS_PATH} &&  git clone https://github.com/NVlabs/nvblox_torch.git && cd nvblox_torch
   sh install.sh $(python -c 'import torch.utils; print(torch.utils.cmake_prefix_path)')
   python -m pip install -e .
   

Examples

To reduce debug printing, use export GLOG_minloglevel=2

1. SDF from dummy map

python examples/get_sdf.py

For the remaining demos, you need to download the Sun3D dataset:

wget http://vision.princeton.edu/projects/2016/3DMatch/downloads/rgbd-datasets/sun3d-mit_76_studyroom-76-1studyroom2.zip -P datasets/3dmatch
unzip datasets/3dmatch/sun3d-mit_76_studyroom-76-1studyroom2.zip -d datasets/3dmatch

2. Sun3D data Segmenting sofa as a dynamic class

python examples/run_mapper.py \
  --dataset "datasets/3dmatch/sun3d-mit_76_studyroom-76-1studyroom2" \
  --dataset-format "sun3d" \
  --voxel-size 0.04 \
  --decay-occupancy-every-n -1 \
  --dynamic-class "sofa" \
  --visualize-voxels

3. Realsense With human segmentation

python examples/run_mapper.py \
    --dataset "sun3d-mit_76_studyroom-76-1studyroom2" \
    --dataset-format realsense \
    --voxel-size 0.04 \
    --decay-occupancy-every-n 1 \
    --dynamic-class person \
    --visualize-voxels

4. Mesh Input Using PyRender to generate depth images. No segmentation.You need to provide your own mesh file here.

python examples/run_mapper.py \
  --dataset "mesh.stl" \
  --dataset-format mesh \
  --voxel-size 0.01 \
  --decay-occupancy-every-n -1 \
  --integrator-type occupancy \
  --visualize-voxels \
  --clear-map-every-n -1