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AirIMU

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# AirIMU : Learning Uncertainty Propagation for Inertial Odometry
[![License: BSD 3-Clause](https://img.shields.io/badge/License-BSD%203--Clause-yellow.svg)](https://raw.githubusercontent.com/haleqiu/AirIMU/main/LICENSE)
[![YouTube](https://img.shields.io/badge/YouTube-b31b1b?style=flat&logo=youtube&logoColor=white)](https://www.youtube.com/watch?v=fTX1u-e7wtU)
[![arXiv](https://img.shields.io/badge/arXiv-AirIMU-orange.svg)](https://arxiv.org/abs/2310.04874)
[![githubio](https://img.shields.io/badge/-homepage-blue?logo=Github&color=FF0000)](https://airimu.github.io/)

![AirIMU](https://raw.githubusercontent.com/haleqiu/AirIMU/main/doc/model.png)
## 📢 Latest News 
- [2025-02-01] Introducing Our New Project!<br>
  🚀 [**AirIO : Learning Inertial Odometry with Enhanced IMU Feature Observability**](https://github.com/Air-IO/Air-IO)<br>
 ```
AirIO achieves up to 86.6% performance boost over SOTA methods:

- ✅ Tailored specifically for drones
- ✅ No external sensors or control inputs required
- ✅ Generalizes to unseen trajectories
- ✅ Explicitly encodes UAV attitude and predicts velocity in body-frame representation
```

## Installation

This work is based on pypose. Follow the instruction and install the newest realase of pypose:
https://github.com/pypose/pypose

## Dataset
> **Note**: Remember to reset the `data_root` in `configs/datasets/${DATASET}/${DATASET}.conf`.

Download the Euroc dataset from:
https://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets

Download the TUM VI dataset from:
https://cvg.cit.tum.de/data/datasets/visual-inertial-dataset

Download the KITTI dataset from:
https://www.cvlibs.net/datasets/kitti/

Download the SubT-MRS dataset: [Download](https://github.com/sleepycan/AirIMU/releases/download/subt_dataset/SubT-MRS-Dataset.zip).   🎈You can find more information about SubT-MRS datasets from the links [here](SubT-MRS-DATASET.md).

## Pretrained Model
> **Note**: You can download our trained ckpt here.

[KITTI](https://github.com/sleepycan/AirIMU/releases/download/pretrained_model/KITTI_odom_model.zip)

[EuRoC](https://github.com/sleepycan/AirIMU/releases/download/pretrained_model_euroc/EuRoCWholeaug.zip)
## Train

Easy way to start the training using the exisiting configuration.
> **Note**:You can also create your own configuration file for different datasets and set the parameters accordingly.

```
python train.py --config configs/exp/EuRoC/codenet.conf
```

More specific option:

```
usage: train.py [-h] [--config CONFIG] [--device DEVICE] [--load_ckpt] [--log]

optional arguments:
  -h, --help       show this help message and exit
  --config CONFIG  config file path
  --device DEVICE  cuda or cpu, Default is cuda:0
  --load_ckpt      If True, try to load the newest.ckpt in the exp_dir specificed in our config file.
  --log            if True, save the meta data with wandb, Default is True
```

## Evaluation

To evaluate the model and generate network inference file net_output.pickle, run the following command:
```
python inference.py --config configs/exp/EuRoC/codenet.conf
```

<br>

You can use the evaluation tool to assess your model performance with net_output.pickle， run the following command.
> **Note**: Make sure to replace path/to/net_output_directory with the directory path where your network output pickle file is stored.

```
python evaluation/evaluate_state.py --dataconf configs/datasets/${DATASET}/${DATASET}.conf --exp path/to/net_output_directory
```

<br>
More specific option for the evaluation tool:

```
usage: evaluation/evaluate_state.py [-h] [--dataconf] [--device] [--exp] [--seqlen] [--savedir] [--usegtrot] [--mask]

optional arguments:
  -h, --help       show this help message and exit
  --config         config file path
  --device         cuda or cpu, Default is cuda:0
  --exp            the directory path where your network output pickle file is stored
  --seqlen         the length of the integration sequence
  --savedir        the save diretory for the evaluation results, default path is  "./result/loss_result"
  --usegtrot       use ground truth rotation for gravity compensation, default is true
  --mask           mask the segments if needed. 
```

### Cite Our Work

Thanks for using our work. You can cite it as:

```bib
@article{qiu2023airimu,
            title={AirIMU: Learning Uncertainty Propagation for Inertial Odometry}, 
            author={Yuheng Qiu and Chen Wang and Can Xu and Yutian Chen and Xunfei Zhou and Youjie Xia and Sebastian Scherer},
            year={2023},
            eprint={2310.04874},
            archivePrefix={arXiv},
            primaryClass={cs.RO}
      }
```

AirIMU

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