EviLOG

KO | EN

gitlite — search

#javascript #python #ai #llm #golang #android #rust #deep-learning #linux #docker #awesome #vue #ios #mcp

(★ 52)

TensorFlow training pipeline and dataset for prediction of evidential occupancy grid maps from lidar point clouds.

EviLOG 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# EviLOG: Evidential Lidar Occupancy Grid Mapping

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

[![IV 2021 Presentation](https://raw.githubusercontent.com/ika-rwth-aachen/EviLOG/main/assets/video_preview.png)](https://www.youtube.com/watch?v=BGbMvHOksBI)

> **A Simulation-based End-to-End Learning Framework for Evidential Occupancy Grid Mapping**
> ([IEEE Xplore](https://ieeexplore.ieee.org/document/9575715), [arXiv](https://arxiv.org/abs/2102.12718))  
>
> [Raphael van Kempen](https://www.ika.rwth-aachen.de/en/institute/staff/raphael-van-kempen-msc.html), [Bastian Lampe](https://www.ika.rwth-aachen.de/en/institute/staff/bastian-lampe-m-sc.html), [Timo Woopen](https://www.ika.rwth-aachen.de/en/institute/management/timo-woopen-msc.html), and [Lutz Eckstein](https://www.ika.rwth-aachen.de/en/institute/management/univ-prof-dr-ing-lutz-eckstein.html)  
> [Institute for Automotive Engineering (ika), RWTH Aachen University](https://www.ika.rwth-aachen.de/en/)
>
> _**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.

[![Demo Video](https://raw.githubusercontent.com/ika-rwth-aachen/EviLOG/main/assets/EviLOG_Real.gif)](https://www.youtube.com/watch?v=lumNOMzdVKc)

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](#installation)
- [Data](#data)
- [Training](#training)

## Installation

We suggest to create a new **[conda](https://docs.conda.io/) environment** with all required packages. This will automatically install the GPU version of TensorFlow with CUDA and cuDNN if an NVIDIA GPU is available.

```bash
# EviLOG/
conda env create -f environment.yml
```

<u>Alternatively</u>, 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.

```bash
# 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 send us a request including your name and organization to evilog@ika.rwth-aachen.de and we will send you a download link.

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

Put the downloaded tar archive into the [data](./data) folder and extract it:

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

## Training

Use the scripts [model/train.py](model/train.py), [model/evaluate.py](model/evaluate.py), and [model/predict.py](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](model/config.yml).

```bash
# 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.

```bash
# 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

![evaluation on test data](https://raw.githubusercontent.com/ika-rwth-aachen/EviLOG/main/assets/EviLOG_Test.gif)

### 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**:

```bash
# 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**:

```bash
# 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).

EviLOG

EviLOG 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

EviLOG

EviLOG 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README