Traversability_Application

System Information

• Operating System: Ubuntu 20.04
• ROS Version: Noetic
• GPU: NVIDIA 3090ti

Clone

git clone --recursive https://github.com/dongjineee/traversability_application.git

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TODO

Gazebo Setup

For setting up the Gazebo simulation environment, clone the Husky repository:

https://github.com/dongjineee/husky

After cloning, follow the instructions on that page to run the Docker setup.

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1. Traditional

 1.a. Image Classification

  GA-Nav: Efficient Terrain Segmentation for Robot Navigation in Unstructured Outdoor Environments

1. Dataset download

Please visit the official websites of the real-world dataset (RELLIS-3D) and the simulation dataset (NEGS-UGV) to download the files. For Data-set, we use ID annotations instead of color annotations. Please refer to the GANav Dataset Directory.

GANav Dataset Directory

GANav
├── data
│   ├── rellis
│   │   │── test.txt
│   │   │── train.txt
│   │   │── val.txt
│   │   │── annotation
│   │   │   ├── 00000 & 00001 & 00002 & 00003 & 00004 
│   │   │── image
│   │   │   ├── 00000 & 00001 & 00002 & 00003 & 00004 
│   ├── rugd
│   │   │── test_ours.txt
│   │   │── test.txt
│   │   │── train_ours.txt
│   │   │── train.txt
│   │   │── val_ours.txt
│   │   │── val.txt
│   │   │── RUGD_annotations
│   │   │   ├── creek & park-1/2/8 & trail-(1 & 3-7 & 9-15) & village
│   │   │── RUGD_frames-with-annotations
│   │   │   ├── creek & park-1/2/8 & trail-(1 & 3-7 & 9-15) & village
│   ├── goose
│   │   ├── goose_label_mapping.csv
│   │   ├── images
│   │   │   ├── train
│   │   │   └── val
│   │   ├── labels
│   │   │   ├── train
│   │   │   └── val
│   │   ├── LICENSE
│   │   ├── test.txt
│   │   ├── train.txt
│   │   │── val.txt
│   ├── lake
│   │   │── test.txt
│   │   │── train.txt
│   │   │── val.txt
│   │   │── annotation
│   │   └── image
├── configs
├── tools

2. Group semantic seg RUN

##=========== wvn docker setting ===========##
cd traversability_application/wild_nav/wild_visual_navigation/docker
docker compose -f docker-compose-gui-nvidia.yaml build
docker compose -f docker-compose-gui-nvidia.yaml up -d
docker compose -f docker-compose-gui-nvidia.yaml exec ga_nav /bin/bash

cd src/GANav-offroad/
pip install -e .

##=========== RUN Data processing ===========##
##for rellis-3d dataset
#run relable group4
python ./tools/convert_datasets/rellis_relabel4.py

#run relable group6
python ./tools/convert_datasets/rellis_relabel6.py

##=========== RUN Training ===========##
##for rellis-3d dataset(real_world)
python ./tools/train.py ./configs/ours/ganav_group6_rellis.py

##for lake dataset(simulation)
python ./tools/train.py ./configs/ours/ganav_group6_lake.py

##=========== RUN Eval ===========##
##for rellis-3d dataset
python ./tools/test.py ./trained_models/rellis_group6/ganav_rellis.py \
          ./work_dirs/ganav_group6_rellis/latest.pth --eval=mIoU

##for lake dataset
python ./tools/test.py ./trained_models/lake_group6/ganav_lake_6.py \
          ./work_dirs/ganav_group6_lake/latest.pth --eval=mIoU

##=========== RUN Visualize ===========##
python ./tools/visualize.py <img_dir> <config> <checkpoint>
##for rellis-3d dataset
python ./tools/visualize.py ./data/rellis/image/00000 ./configs/ours/ganav_group6_rellis.py ./work_dirs/ganav_group6_rellis/latest.pth

##for lake dataset
python ./tools/visualize.py ./data/lake/image ./configs/ours/ganav_group6_lake.py ./work_dirs/ganav_group6_lake/latest.pth

3. ROS_PKG RUN

# In the sim_container
roslaunch husky_gazebo husky_lake.launch rviz:=ga_nav

# In the ga_nav_container
roslaunch ga_nav ga_nav.launch

 1.b. DEM Based

  BGKI-Traversability

  ToDo

  Elevation-Mapping

  ToDo

2. Learning Based

 2.a. Image Based

  Fast Traversability Estimation for Wild Visual Navigation

Package RUN

##=========== wvn docker setting ===========##
cd traversability_application/wild_nav/wild_visual_navigation/docker
docker compose -f docker-compose-gui-nvidia.yaml build
docker compose -f docker-compose-gui-nvidia.yaml up -d
docker compose -f docker-compose-gui-nvidia.yaml exec wvn_nvidia /bin/bash
source first_run.sh

##=========== RUN SIMULATION ===========##
# In the sim_container
roslaunch husky_gazebo husky_lake.launch rviz:=wild_nav

# In the wvn_container
roslaunch wild_visual_navigation_jackal wild_visual_navigation.launch

 2.b. Geometric Based

  Learning Self-Supervised Traversability With Navigation Experiences of Mobile Robots: A Risk-Aware Self-Training Approach

Package RUN

cd ~/LeSTA/docker
##image build
docker compose -f docker-compose-gui-nvidia.yaml build
##create container
docker compose -f docker-compose-gui-nvidia.yaml up -d
docker exec -it docker-lesta-1 /bin/bash

##in container
cd ~/lesta_ws/
catkin build
source devel/setup.bash

## label_generate ##
roslaunch lesta label_generation.launch
rosservice call /lesta/save_label_map "train_set.pcd" "/root/lesta_ws/src/LeSTA/data/train"
rosservice call /lesta/save_label_map "val_set.pcd" "/root/lesta_ws/src/LeSTA/data/val"

## train ##
cd /root/lesta_ws/src/LeSTA
python3 pylesta/tools/train.py

## travel predict ##
roslaunch lesta traversability_prediction.launch
roslaunch lesta traversability_mapping.launch

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Real World - Dataset

• RELLIS-3D : Data with Stereo Camera images, LiDAR pointclouds, GPS/IMU
• RUGD : Video dataset annotated with pixel-wise labels

Simulation World - Dataset

• NEGS-UGV : semantic & rgb images, seamantic & raw Lidar pointsclouds