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https://github.com/Ironbrotherstyle/UnVIO
The source code of IJCAI2020 paper "Unsupervised Monocular Visual-inertial Odometry Network".
https://github.com/Ironbrotherstyle/UnVIO
Last synced: 27 days ago
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The source code of IJCAI2020 paper "Unsupervised Monocular Visual-inertial Odometry Network".
- Host: GitHub
- URL: https://github.com/Ironbrotherstyle/UnVIO
- Owner: Ironbrotherstyle
- Created: 2020-03-22T09:58:05.000Z (about 4 years ago)
- Default Branch: master
- Last Pushed: 2023-08-23T16:18:53.000Z (10 months ago)
- Last Synced: 2024-02-10T05:04:42.863Z (4 months ago)
- Language: Python
- Size: 115 MB
- Stars: 44
- Watchers: 2
- Forks: 6
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
Lists
- Awesome-SLAM - Unsupervised Monocular Visual-inertial Odometry Network
README
# Unsupervised Network for Visual Inertial Odometry
IJCAI2020 paper: Unsupervised Network for Visual Inertial Odometry.
| KITTI 09 | KITTI 10 |
| ------------- | ------------- |
|  |  |
## Introduction
This repository is the official [Pytorch](https://pytorch.org/) implementation of IJCAI2020 paper [Unsupervised Network for Visual Inertial Odometry](https://robotics.pkusz.edu.cn/static/papers/IJCAI-weipeng.pdf).
## Installation
UnVIO has been tested on Ubuntu with Pytorch 1.4 and Python 3.7.10. For installation, it is recommended to use conda environment.
```shell
conda create -n unvio_env python=3.7.10
conda activate unvio_env
pip install -r requirements.txt
```
Other applications should be installed also,
```shell
sudo apt install gnuplot
```
## Data Preparing
The datasets used in this paper are [KITTI raw](http://www.cvlibs.net/datasets/kitti/raw_data.php) ataset
and [Malaga](https://www.mrpt.org/MalagaUrbanDataset) dataset. Please refer to [Data preparing](DATA.md) for detailed
instruction.
## Validation
Validation can be implemented on Depth estimation and Odometry estimation.
First specify the model path and dataset path:
```shell
ROOT='MODEL_ROOT_HERE'
DATA_ROOT='DATA_ROOT_HERE'
```
### Depth Estimation
For Depth estimation on KITTI 09 (if you want to test on KITTI 10, change the
`--dataset-list` to `.eval/kitti_10.txt`, same set for Malaga dataset), run the following command:
```shell
ROOT=$ROOT/kitti_ckpt
#ROOT=$ROOT/malaga_ckpt
DATA_ROOT=$DATA_ROOT/KITTI_rec_256/
#DATA_ROOT=$DATA_ROOT/Malaga_down/
```
```shell
python test_disp.py \
--pretrained-dispnet $ROOT/UnVIO_dispnet.pth.tar \
--dataset-dir $DATA_ROOT \
--dataset-list .eval/kitti_09.txt \
--output-dir $ROOT/results_disp \
--save-depth
```
The `predictions.npy` that stores the all the depth values will be saved in `$ROOT/results_disp`, if `--save-depth` is added, the colored depths will be saved simultaneously is `$ROOT/results_disp/disp`
### Visual Odometry
For Odometry estimation KITTI 09 (if you want to test on KITTI 10, change the `testscene` to `2011_09_30_drive_0034_sync_02`), run the following command:
```shell
ROOT=$ROOT/kitti_ckpt
DATA_ROOT=$DATA_ROOT
```
```shell
python test_pose.py \
--pretrained-visualnet $ROOT/UnVIO_visualnet.pth.tar \
--pretrained-imunet $ROOT/UnVIO_imunet.pth.tar\
--pretrained-posenet $ROOT/UnVIO_posenet.pth.tar\
--dataset_root $DATA_ROOT \
--dataset KITTI \
--testscene 2011_09_30_drive_0033_sync_02 \
--show-traj
```
This will create a `.csv` file represneting $T_{wc} \in \mathbb{R}^{3 \times 4}$ in `$ROOT` directory. If the `--show-traj` is added, a scaled trajectory comparing with the ground truth will be ploted.
## Train
Run the following command to train the UnVIO from scratch:
```shell
DATA_ROOT=$DATA_ROOT
```
```shell
python train.py --dataset_root $DATA_ROOT --dataset KITTI
```
specify `--dataset (KITTI or Malaga)` as you need.
## Citation
```
@inproceedings{2020Unsupervised,
title={Unsupervised Monocular Visual-inertial Odometry Network},
author={ Wei, P. and Hua, G. and Huang, W. and Meng, F. and Liu, H. },
booktitle={Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}},
year={2020},
}
```
## License
This project is licensed under the terms of the MIT license.
## References
The repository borrowed some code from [SC](https://github.com/JiawangBian/SC-SfMLearner-Release), [Monodepth2](https://github.com/nianticlabs/monodepth2.git) and [SfMLearner](https://github.com/ClementPinard/SfmLearner-Pytorch), thanks for their great work.