https://github.com/pekkaran/violet

A toy stereo visual inertial odometry (VIO) system
https://github.com/pekkaran/violet

computer-vision ekf extended-kalman-filter kalman-filter msckf slam vio visual-inertial-odometry

Last synced: about 1 month ago
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A toy stereo visual inertial odometry (VIO) system

• Host: GitHub
• URL: https://github.com/pekkaran/violet
• Owner: pekkaran
• License: gpl-3.0
• Created: 2022-03-14T16:38:36.000Z (almost 3 years ago)
• Default Branch: main
• Last Pushed: 2023-04-28T18:58:28.000Z (almost 2 years ago)
• Last Synced: 2024-09-24T16:09:53.331Z (5 months ago)
• Topics: computer-vision, ekf, extended-kalman-filter, kalman-filter, msckf, slam, vio, visual-inertial-odometry
• Language: Rust
• Homepage:
• Size: 328 KB
• Stars: 7
• Watchers: 1
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Violet

A toy stereo visual inertial odometry (VIO) system. Built upon [HybVIO](https://github.com/SpectacularAI/HybVIO), which was built upon [PIVO](https://arxiv.org/pdf/1708.00894.pdf), which was built upon [MSCKF by Mourikis and Roumeliotis](https://www-users.cse.umn.edu/~stergios/papers/ICRA07-MSCKF.pdf).

## Status

The implementation of the VIO is not yet complete, but some of the sub-components have been tested to work:

- [X] Data input:

  - [X] IMU parsing

  - [X] Video decoding

  - [X] Calibration parsing

- [X] Stereo feature tracker:

  - [X] Feature detection: FAST corners

  - [X] Optical flow: Pyramidal Lucas-Kanade

  - [X] Track spatial distribution enhancement

- [ ] Odometry: Extended Kalman Filter (EKF)

  - [X] Prediction step (the dynamic model)

  - [X] Update step

  - [X] Stationarity update

  - [ ] Visual update

- [X] Camera models: Pinhole with radial distortion

- [X] Visualizations

  - [X] Various 2D visualizations

  - [X] VIO output track visualization, 3D

Goals:

* Do not depend on any computer vision libraries such as OpenCV, implement all the relevant algorithms from scratch.

* Demonstrate plausibility and advantages of using Rust over C++ for this kind of real-time sensor fusion applications. Consequently a goal is to make the implementation run relatively fast.

Non-goals:

* Simultaneous Localization and Mapping (SLAM).

* State-of-the-art accuracy and robustness. For a commercial-grade solution, please see [Spectacular AI](https://www.spectacularai.com/).

## Obtaining test data

The input data format supported by the VIO is the same as described [here](https://github.com/AaltoML/vio_benchmark#benchmark-data-format), except that the calibration is read from a JSON format. A conversion for the [EuRoC](https://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets) dataset exists:

* Install `ffmpeg`

* Install Python dependencies, eg by `pip install pyyaml numpy`

* Run download and conversion, eg by: `python scripts/download_euroc.py --fast --case v1-01-easy`

The converted datasets are saved under `data/benchmark/`. Note that about 10 GB of space is required for converting all the EuRoC datasets.

To implement conversion for other datasets, please examine format of the converted EuRoC datasets.

## Running the VIO

Install Rust, see for example the official [get started](https://www.rust-lang.org/learn/get-started) guide. Clone this git repository and run from its root:

```bash

cargo run -- -i data/benchmark/euroc/v1-01-easy --show-tracks

```

See all the available visualizations and options with:

```bash

cargo run -- --help

```

## License

Licensed under **GPLv3**. Note that the algorithms are heavily based on [HybVIO](https://github.com/SpectacularAI/HybVIO) which is licensed under GPLv3.