MLF-VO

KO | EN

gitlite — search

#ai #typescript #llm #golang #rust #linux #macos #windows #awesome #cli #openai #ios #mcp #open-source

(★ 20)

Multi-Layer Fusion Visual Odometry

MLF-VO 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# MLF-VO

This repo implements the network described in the ICRA2022 paper:

[Self-Supervised Ego-Motion Estimation Based on Multi-Layer Fusion of RGB and Inferred Depth](https://arxiv.org/pdf/2203.01557.pdf)

Zijie Jiang, Hajime Taira, Naoyuki Miyashita and Masatoshi Okutomi

Please also visit our [project page](http://www.ok.sc.e.titech.ac.jp/res/MLF-VO/).

If you find our work useful for your research, please consider citing the following paper:

```
@inproceedings{jiang2022mlfvo,
  title={Self-Supervised Ego-Motion Estimation Based on Multi-Layer Fusion of RGB and Inferred Depth},
  author={Jiang, Zijie and Taira, Hajime and Miyashita, Naoyuki and Okutomi, Masatoshi},
  booktitle={2022 IEEE International Conference on Robotics and Automation (ICRA)},
  year={2022}
}
```

# Updates

- [x] 2022.05.20. Pretrained weights and demo for testing are open.

# Requirements

Our experiments are conducted on a machine installed with Ubuntu 18.04, Pytorch 1.7.1, CUDA 10.1. You can install other required packages by:

``` bash
pip install requirements.txt
```

Since our demo partly depends on [Monodepth2](https://github.com/nianticlabs/monodepth2) and [CEN](https://github.com/yikaiw/CEN), clone this repository with `--recurse` option. Please also refer their original repositories.

# Prepare dataset

Please download the KITTI odometry benchmark from their [site](http://www.cvlibs.net/datasets/kitti/eval_odometry.php) and create a soft link by executing the following command:

``` bash
ln -s /path/to/dataset/data_odometry_color/dataset data
```

# Odometry evaluation

Download and place the pretrained weights of models following:

``` bash
wget http://www.ok.sc.e.titech.ac.jp/res/MLF-VO/models.zip
unzip -d data models.zip
rm models.zip
```

The demo specifies several paths and configurations in `configs/run_odometry.yaml`. Please modify line 2, 6, or 9 to use your own paths to dataset, model weights, or outputs.

``` bash
python run_odometry -c configs/run_odometry.yaml
```

The estimated trajectory will be exported to `data/outputs/09.txt`. Each line in the file represents the estimated camera, corresponding to each frame in the sequence, in the following format:

```
T11 T12 T13 T14 T21 T22 T23 T24 T31 T32 T33 T34
...
```

where T11, T12, ... , T34 are the elements of 3x4 camera transformation matrix (from camera to world coordinate system):

```
T11 T12 T13 T14
T21 T22 T23 T24
T31 T32 T33 T34
```

We recommend to use this [toolbox](https://github.com/Huangying-Zhan/kitti-odom-eval) to evaluate the inferred trajectory.

# Training

Please checkout to the `dev` branch, and run

```
python train.py --data_path ~/Documents/datasets/kitti_odom/dataset --log_dir ~/Documents/MLF-VO/KITTI --model_name test --num_epochs 20 --dataset kitti_odom --split odom
```

Please change L55 in `trainer.py` to the wanted type of the PoseNet.

```python
self.models['pose'] = posenet_type_dict[config_dict.model.type]()
```

# Acknowledgement

We are grateful to the authors of [Monodepth2](https://github.com/nianticlabs/monodepth2) and [CEN](https://github.com/yikaiw/CEN) for publicly sharing their codes.

# License

MLF-VO is released under GPLv3 License.

MLF-VO

MLF-VO 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

MLF-VO

MLF-VO 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README