This repository contains the code of the implementation of the article "TE-NeXt: A LiDAR-Based 3D Sparse Convolutional Network for Traversability Estimation". This article develops a novel and efficient architecture focused on traversability estimation, which adopts an encoder-decoder configuration and whose main innovation lies in the convolutional residual block designed, taking into consideration notions of current trends in the world of deep learning, such as vision Transformers and Sparse convolutions. The following image shows the TE-NeXt architecture.

- Ubuntu >= 14.04
- CUDA >= 10.1.243 and the same CUDA version used for pytorch (e.g. if you use conda cudatoolkit=11.1, use CUDA=11.1 for MinkowskiEngine compilation)
- pytorch >= 1.7 You must match the CUDA version pytorch uses and CUDA  version used for Minkowski Engine installation.
- python >= 3.6
- ninja (for installation)
- GCC >= 7.4.0

The Minkowski Engine is distributed via PyPI MinkowskiEngine which can be installed simply with pip. First, install pytorch following the instruction. Next, install openblas.

sudo apt install build-essential      
python3-dev libopenblas-dev
pip install torch ninja
pip install -U MinkowskiEngine --install-option="--blas=openblas" -v --no-deps

For pip installation from the latest source

pip install -U git+https://github.com/NVIDIA/MinkowskiEngine --no-deps

git clone https://github.com/NVIDIA/MinkowskiEngine.git
cd MinkowskiEngine
python setup.py install --cpu_only

For the correct functioning of the repository the configuration files must be filled in.

• The config folder contains the .yaml files that determine which data from each dataset is intended for training or testing and the TE-NeXt training configuration.

• The model folder contains the implementation of TE-NeXt

• Afterwards the dataset.py file read and prepare the dato for the network. If you wish change the features which we are training the network you should change this script.

• Once the data is ready and its clear the neural network input, the training process is described in training.py.

  • The folders where are located the datasets can be changed in train.py or given by arguments.
  • For running the training:

  • python3 training.py
    

  • For running the test:

  • python3 test.py