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Proprioception Is All You Need: Terrain Classification for Boreal Forests
Proprioception Is All You Need: Terrain Classification for Boreal Forests
Damien LaRocque, William Guimont-Martin, David-Alexandre Duclos, Philippe Giguère, Francois Pomerleau
Paper: https://arxiv.org/abs/2403.16877
This repo contains the source code and the datasets used in our paper Proprioception Is All You Need: Terrain Classification for Boreal Forests that was submitted to the 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024).
Source Code
Most functions are available in the utils
directory :
utils/preprocessing.py
contains the functions for the preprocessing pipelineutils/models.py
contains the code for the trained modelsutils/frequency.py
contains the functions for the multi-channel spectrogram
Installation
We recommend installing the dependencies of the repo in a virtual environment.
Two dependencies, mamba-ssm
and causal-conv1d
require a NVIDIA GPU.
python3.10 -m venv .venv
. .venv/bin/activate
pip install -r requirements.txt
We also provide a Dockerfile
and a DockerfileGPU
to build a Docker image with all the dependencies.
# Build the Docker image
docker build -t borealtc-gpu -f DockerfileGPU .
# Run the Docker image
docker run --gpus all -e CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES --rm --ipc host \
--mount type=bind,source=.,target=/code/ \
--mount type=bind,source=/dev/shm,target=/dev/shm \
borealtc-gpu python3 main.py
Dataset
The data
directory contains two different datasets:
- the
BorealTC
dataset, our publicly available dataset - the
Vulpi
dataset, from the 2021 paper of Vulpi et al. This dataset is originally sourced from their repository Ph0bi0/T_DEEP. The original data is available in the form of.mat
files. We converted their data in*.csv
files to work in Python.
Each experiment is provided in two files imu_XX.csv
and pro_XX.csv
, for the IMU and the wheel service data, respectively.
Both datasets are organized as the following :
data
├── borealtc
│ ├── CLASS1
│ │ ├── imu_00.csv
│ │ ├── imu_01.csv
│ │ ├── ...
│ │ ├── pro_00.csv
│ │ ├── pro_01.csv
│ │ └── ...
│ └── CLASS2
│ ├── imu_00.csv
│ ├── imu_01.csv
│ ├── ...
│ ├── pro_00.csv
│ ├── pro_01.csv
│ └── ...
└── vulpi
├── CLASS1
│ ├── imu_1.csv
│ ├── imu_2.csv
│ ├── ...
│ ├── pro_1.csv
│ ├── pro_2.csv
│ └── ...
└── CLASS2
├── imu_1.csv
├── imu_2.csv
├── ...
├── pro_1.csv
├── pro_2.csv
└── ...
Model hyperparameters and checkpoints
Mamba
Dataset | Data Type | d_state | d_conv | expand | d_model | Norm epsilon | Initial LR | LR drop factor | Reduce LR patience | Max epochs | Minibatch size | Valid patience | Gradient threshold | Focal loss alpha | Focal loss gamma | Checkpoint |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Vulpi | IMU <br/> Wheel service | 56 <br/> 56 | 3 <br/> 3 | 4 <br/> 4 | 56 <br/> 56 | 5e-3 | 5e-3 | 0.33 | 4 | 60 | 64 | 8 | 2 | 0.75 | 2 | mamba_vulpi.ckpt |
BorealTC | IMU <br/> Wheel service | 16 <br/> 16 | 4 <br/> 3 | 4 <br/> 6 | 32 <br/> 8 | 6.3e-6 | 1.5e-3 | 0.25 | 4 | 60 | 16 | 8 | None | 0.75 | 2.25 | mamba_borealtc.ckpt |
Combined | IMU <br/> Wheel service | 16 <br/> 16 | 4 <br/> 3 | 4 <br/> 6 | 32 <br/> 8 | 6.3e-6 | 1.5e-3 | 0.25 | 4 | 60 | 16 | 8 | None | 0.75 | 2.25 | mamba_combined.ckpt |
In the above table, d_state
, d_conv
, expand
and d_model
are parameters specific to Mamba. During optimization, each data type branch can have separate parameters.
CNN
The CNN model used the same hyperparameters for both datasets and their combination. Here are the hyperparameters used for the CNN model:
Parameter | Value |
---|---|
Using Hamming Window | True |
Initial Learning Rate | 5e-3 |
Learning Rate Scheduler | ReduceLROnPlateau |
Scheduler LR Factor | 0.1 |
Scheduler Patience | 4 |
Max Epochs | 150 |
Early Stopping Patience | 8 |
Gradient Clipping Value | 6 |
Acknowledgments
We would like to acknowledge Fabio Vulpi, Annalisa Milella, Roberto Marani and Giulio Reina for their paper Recurrent and convolutional neural networks for deep terrain classification by autonomous robots. This repo started as a fork of Ph0bi0/T_DEEP, their repo, which contains the source code and the dataset of their paper.
Citation
If you use the code or data in an academic context, please cite the following work:
@article{LaRocque2024BorealTC,
title={Proprioception Is All You Need: Terrain Classification for Boreal Forests},
author={Damien LaRocque and William Guimont-Martin and David-Alexandre Duclos and Philippe Giguère and François Pomerleau},
journal={arXiv preprint arXiv:2403.16877},
year={2024},
eprint={2403.16877},
archivePrefix={arXiv},
primaryClass={cs.RO}
}
License
This project is licensed under a MIT license.