This repository contains the associated code for the paper "VAST: Visual and Spectral Terrain Classification in Unstructured Multi-Class Environments", presented at IROS 2022 in Kyoto, Japan.
# Install requirements (can be done in a virtual workspace)
cd vast_terrain_classification
pip3 install -r requirements.txt
# Clone the data repository inside the classification repo
git clone git@github.com:RIVeR-Lab/vast_data.git data
# Training Individual Network Features
python3 train_imu.py
python3 train_spec.py
python3 train_img.py
# Edit final networks names in gen_fused_features.py
# Generate the fused features from individual networks
python3 scripts/gen_fused_features.py
# Create Test Train Indices
python3 scripts/create_test_train_indices.py
# Train Fused Network
python3 train_fused_net.py
# Evaluate Fused Network
python3 eval_fused_net.py
If you use the methodologies or data associated with this paper, please include the following citation:
@inproceedings{
hanson2022vast,
title={Vast: Visual and spectral terrain classification in unstructured multi-class environments},
author={Hanson, Nathaniel and Shaham, Michael and Erdo{\u{g}}mu{\c{s}}, Deniz and Padir, Ta{\c{s}}kin},
booktitle={2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
pages={3956--3963},
year={2022},
organization={IEEE}
}
We welcome feedback and questions regarding our work. For code related questions, please open an issue with GitHub. For research questions or collaborations, please contact the corresponding author at hanson [.] n [@] northeastern [.] edu.