GANav: Efficient Terrain Segmentation for Robot Navigation in Unstructured Outdoor Environments (Accepted by RAL/IROS 2022)
This is the code base for:
GANav: Efficient Terrain Segmentation for Robot Navigation in Unstructured Outdoor Environments.
Tianrui Guan, Divya Kothandaraman, Rohan Chandra, Adarsh Jagan Sathyamoorthy, Kasun Weerakoon, Dinesh Manocha
07-25-2022: The latest version of GA-Nav is ready. Please make sure to pull the latest changes. Please refer to ./trained_models folder and new model is available here.
06-17-2022: Accepted by RAL/IROS.
02-21-2022: Updated code and checkpoints in preparation.
12-09-2021: Updated ros-support for GANav here.
05-10-2021: Trained models are ready. Please refer to ./trained_models folder. Please download the trained model here.
If you find this project useful in your research, please cite our work:
@ARTICLE{9810192,
author={Guan, Tianrui and Kothandaraman, Divya and Chandra, Rohan and Sathyamoorthy, Adarsh Jagan and Weerakoon, Kasun and Manocha, Dinesh},
journal={IEEE Robotics and Automation Letters},
title={GA-Nav: Efficient Terrain Segmentation for Robot Navigation in Unstructured Outdoor Environments},
year={2022},
volume={7},
number={3},
pages={8138-8145},
doi={10.1109/LRA.2022.3187278}}Our video can be found here.
We propose GANav, a novel group-wise attention mechanism to identify safe and navigable regions in off-road terrains and unstructured environments from RGB images. Our approach classifies terrains based on their navigability levels using coarse-grained semantic segmentation. Our novel group-wise attention loss enables any backbone network to explicitly focus on the different groups' features with low spatial resolution. Our design leads to efficient inference while maintaining a high level of accuracy compared to existing SOTA methods.
Our extensive evaluations on the RUGD and RELLIS-3D datasets shows that GANav achieves an improvement over the SOTA mIoU by 2.25-39.05% on RUGD and 5.17-19.06% on RELLIS-3D. We interface GANav with a deep reinforcement learning-based navigation algorithm and highlight its benefits in terms of navigation in real-world unstructured terrains. We integrate our GANav-based navigation algorithm with ClearPath Jackal and Husky robots, and observe an increase of 10% in terms of success rate, 2-47% in terms of selecting the surface with the best navigability and a decrease of 4.6-13.9% in trajectory roughness. Further, GANav reduces the false positive rate of forbidden regions by 37.79%. Code, videos, and a full technical report are available at https://gamma.umd.edu/offroad/.
conda create -n ganav python=3.7 -y
conda activate ganav
conda install pytorch=1.6.0 torchvision cudatoolkit=10.1 -c pytorch
# or use
# conda install pytorch=1.10.0 torchvision cudatoolkit=11.3 -c pytorch
pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu101/torch1.6.0/index.html
# or use
# pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu113/torch1.10.0/index.html
Note: Make sure you mmcv version is compatible with your pytorch and cuda version. In addition, you can specify the MMCV verion (1.3.16).
git clone https://github.com/rayguan97/GANav-offroad.git
cd GANav-offroad
pip install einops prettytable
pip install -e .
In this section, we explain the data generation process and how to train and test our network.
To be able to run our network, please follow those steps for generating processed data.
Please go to RUGD and RELLIS-3D (we use the ID annotation instead of color annotation for RELLIS-3D) officail website to download their data. Download the GOOSE dataset, and extract all zips in data/goose to match the below structure.
Note: Since RELLIS-3D dataset has been updated, please run python ./tools/process_rellis.py to remove "pylon_camera_node/", "pylon_camera_node_label_id/" folder, after structure the data as follows:
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
├── configs
├── tools
...
In this step, we need to process the groundtruth labels, as well as generating the grouped labels.
For RELLIS-3D dataset, run:
python ./tools/convert_datasets/rellis_relabel[x].py
For RUGD dataset, run:
python ./tools/convert_datasets/rugd_relabel[x].py
Replease [x] with 4 or 6, to generated data with 4 annotation groups or 6 annotation groups.
For the GOOSE dataset, run:
python ./tools/convert_datasets/goose_relabel6.py
To train a model on RUGD datasets with our methods on 6 groups:
python ./tools/train.py ./configs/ours/ganav_group6_rugd.py
Please modify ./configs/ours/* to play with your model and read ./tools/train.py for more details about training options.
To train a model on multiple GPUs(RUGD, 6 groups, 2 GPUs):
./tools/dist_train.sh ./configs/ours/ganav_group6_rugd.py 2
To train on the GOOSE dataset run:
python ./tools/train.py ./configs/ours/ganav_group6_goose.py
An example to evaluate our method with 6 groups on RUGD datasets with mIoU metrics:
python ./tools/test.py ./trained_models/rugd_group6/ganav_rugd_6.py \
./trained_models/rugd_group6/ganav_rugd_6.pth --eval=mIoU
Please read ./tools/test.py for more details.
This project is released under the Apache 2.0 license.
The source code of GANav is heavily based on MMSegmentation.
