EviLOG: Evidential Lidar Occupancy Grid Mapping

This repository provides the dataset as well as the training pipeline that was used in our paper:

A Simulation-based End-to-End Learning Framework for Evidential Occupancy Grid Mapping (IEEE Xplore, arXiv)

Raphael van Kempen, Bastian Lampe, Timo Woopen, and Lutz Eckstein
Institute for Automotive Engineering (ika), RWTH Aachen University

Abstract — Evidential occupancy grid maps (OGMs) are a popular representation of the environment of automated vehicles. Inverse sensor models (ISMs) are used to compute OGMs from sensor data such as lidar point clouds. Geometric ISMs show a limited performance when estimating states in unobserved but inferable areas and have difficulties dealing with ambiguous input. Deep learning-based ISMs face the challenge of limited training data and they often cannot handle uncertainty quantification yet. We propose a deep learning-based framework for learning an OGM algorithm which is both capable of quantifying uncertainty and which does not rely on manually labeled data. Results on synthetic and on real-world data show superiority over other approaches.

We hope our paper, data and code can help in your research. If this is the case, please cite:

@INPROCEEDINGS{9575715,
  author={van Kempen, Raphael and Lampe, Bastian and Woopen, Timo and Eckstein, Lutz},
  booktitle={2021 IEEE Intelligent Vehicles Symposium (IV)}, 
  title={A Simulation-based End-to-End Learning Framework for Evidential Occupancy Grid Mapping}, 
  year={2021},
  pages={934-939},
  doi={10.1109/IV48863.2021.9575715}}

Content

• Installation
• Data
• Training

Installation

We suggest to create a new conda environment with all required packages. This will automatically install the GPU version of TensorFlow with CUDA and cuDNN if an NVIDIA GPU is available.

# EviLOG/
conda env create -f environment.yml

Alternatively, it is possible to install all package dependencies in a Python 3.7 environment (e.g. by using virtualenv) with pip. Note that CMake must be installed to build the point-pillars package.

# EviLOG/
pip install -r requirements.txt

Data

We provide all data that is required to reproduce the results in our paper. The EviLOG dataset comprises:

• Synthetic training and validation data consisting of lidar point clouds (as pcd files) and evidential occupancy grid maps (as png files)
  • 10.000 training samples
  • 1.000 validation samples
  • 100 test samples
• Real-world input data that was recorded with a Velodyne VLP32C lidar sensor during a ~9 minutes ride in an urban area (5.224 point clouds).

We are very interested in the impact of our provided dataset. Please fill out the dataset request form and we will send you a download link that is valid for 24 hours.

Note: Download size is approximately 6.8 GB, uncompressed size is approximately 11.8 GB.

Put the downloaded tar archive into the data folder and extract it:

# EviLOG/data/
tar xvf EviLOG_2021.tar.gz

Training

Use the scripts model/train.py, model/evaluate.py, and model/predict.py to train a model, evaluate it on validation data, and make predictions on a testing dataset or the provided real-world input point clouds.

Input directories, training parameters, and more can be set via CLI arguments or in a config file. Run the scripts with --help-flag or see one of the provided exemplary config files for reference.

Training

Start training the model by passing the provided config file model/config.yml.

# EviLOG/model/
export TF_FORCE_GPU_ALLOW_GROWTH=true  # try this if cuDNN fails to initialize
./train.py -c config.yml

You can visualize training progress by pointing TensorBoard to the output directory (model/output by default). Training metrics will also be printed to stdout.

Evaluation

Before evaluating your trained model on the test data, set the parameter model-weights to point to the best_weights.hdf5 file in the Checkpoints folder of its model directory.

# EviLOG/model/
./evaluate.py -c config.yml --input-validation ../data/input_test --label-validation ../data/label_test --model-weights output/<YOUR-TIMESTAMP>/Checkpoints/best_weights.hdf5

The evaluation results will be exported to the Evaluation folder in your model directory. This also comprises a comparison between occupancy grid maps predicted by the neural network and grid maps created using a simple geometric inverse sensor model.

Left: Input lidar point cloud. Middle: baseline OGM created by geometric ISM. Right: OGM predicted by deep ISM

Testing

To actually see the predictions your network makes, try it out on unseen input point clouds, such as the provided test data or real-world input point clouds. The predicted occupancy grid maps are exported to the directory specified by the parameter output-dir-testing.

Prediction using synthetic test data:

# EviLOG/model/
./predict.py -c config.yml --model-weights output/<YOUR-TIMESTAMP>/Checkpoints/best_weights.hdf5 --prediction-dir output/<YOUR-TIMESTAMP>/Predictions

Prediction using real-world input point clouds:

# EviLOG/model/
./predict.py -c config.yml --input-testing ../data/input_real --model-weights output/<YOUR-TIMESTAMP>/Checkpoints/best_weights.hdf5 --prediction-dir output/<YOUR-TIMESTAMP>/Predictions-Real

Acknowledgement

This research is accomplished within the project ”UNICARagil” (FKZ 16EMO0289). We acknowledge the financial support for the project by the Federal Ministry of Education and Research of Germany (BMBF).