Prerequisite:
- install ROS.
- install PyTorch.
- install torchvision.
If you want to use only semantic cloud segmentation node just build the package in a catkin workspace, for example:
mkdir -p ~/catkin_ws/src
cd ~/catkin_ws/src
git clone https://github.com/ctu-vras/traversability_estimation
git clone https://github.com/ctu-vras/cloud_proc
cd ~/catkin_ws/
rosdep install --from-paths /catkin_ws --ignore-src --rosdistro noetic -y
catkin build
In case you would like to run geometric cloud segmentation, traversability fusion or image segementation to point cloud projection nodes, please follow the extended proceedure (requires access to another repositories):
- Install vcstool for workspace creation:
sudo apt install python3-vcstool
- Create and build ROS workspace:
cd ~/catkin_ws/ vcs import src < src/traversability_estimation/config/workspace.repos catkin config -DCMAKE_BUILD_TYPE=Release catkin build
Put the weights to ./config/weights/ folder:
./config/weights/
├── hrnetv2_w48_imagenet_pretrained.pth
├── seg_hrnet_ocr_w48_train_512x1024_sgd_lr1e-2_wd5e-4_bs_12_epoch484/
├── depth_cloud/
└── smp/
└── se_resnext50_32x4d_352x640_lr1e-4.pth
One may also download datasets to train images and point cloud segmentation models. Please, refer to ./docs/rellis.md or ./docs/trav_data.md for examples.