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https://github.com/stella-cv/stella_vslam

This is a unofficial fork of OpenVSLAM (https://github.com/xdspacelab/openvslam)
https://github.com/stella-cv/stella_vslam
visual-slam
Last synced: about 1 month ago
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This is a unofficial fork of OpenVSLAM (https://github.com/xdspacelab/openvslam)
Host: GitHub
URL: https://github.com/stella-cv/stella_vslam
Owner: stella-cv
License: other
Created: 2021-01-30T03:16:19.000Z (over 3 years ago)
Default Branch: main
Last Pushed: 2024-05-11T04:11:58.000Z (about 1 month ago)
Last Synced: 2024-05-16T10:36:33.227Z (about 1 month ago)
Topics: visual-slam
Language: C++
Homepage: https://stella-cv.rtfd.io/en/latest/
Size: 4.14 MB
Stars: 836
Watchers: 35
Forks: 364
Open Issues: 21
Metadata Files:
- Readme: README.md
- License: LICENSE
Lists

Awesome-SLAM - OpenVSLAM: A Versatile Visual SLAM Framework
README

        # stella_vslam

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[![License](https://img.shields.io/badge/License-BSD%202--Clause-orange.svg)](https://opensource.org/licenses/BSD-2-Clause)

---

> *NOTE:* This is a community fork of [xdspacelab/openvslam](https://github.com/xdspacelab/openvslam). It was created to continue active development of OpenVSLAM on Jan 31, 2021. The original repository is no longer available. Please read the [official statement of termination](https://github.com/xdspacelab/openvslam/wiki/Termination-of-the-release) carefully and understand it before using this. The similarities with ORB_SLAM2 in the original version have been removed by [#252](https://github.com/stella-cv/stella_vslam/pull/252). If you find any other issues with the license, please point them out. See [#37](https://github.com/stella-cv/stella_vslam/issues/37) and [#249](https://github.com/stella-cv/stella_vslam/issues/249) for discussion so far.

*Versions earlier than 0.3 are deprecated. If you use them, please use them as a derivative of ORB_SLAM2 under the GPL license.*

---

## Overview

[](https://arxiv.org/abs/1910.01122)



[**[PrePrint]**](https://arxiv.org/abs/1910.01122)

stella_vslam is a monocular, stereo, and RGBD visual SLAM system.

### Features

The notable features are:

- It is compatible with **various type of camera models** and can be easily customized for other camera models.

- Created maps can be **stored and loaded**, then stella_vslam can **localize new images** based on the prebuilt maps.

- The system is fully modular. It is designed by encapsulating several functions in separated components with easy-to-understand APIs.

- We provided **some code snippets** to understand the core functionalities of this system.

One of the noteworthy features of stella_vslam is that the system can deal with various type of camera models, such as perspective, fisheye, and equirectangular.

If needed, users can implement extra camera models (e.g. dual fisheye, catadioptric) with ease.

For example, visual SLAM algorithm using **equirectangular camera models** (e.g. RICOH THETA series, insta360 series, etc) is shown above.

We provided [documentation](https://stella-cv.readthedocs.io/) for installation and tutorial.

The repository for the ROS wrapper is [stella_vslam_ros](https://github.com/stella-cv/stella_vslam_ros).

### Acknowledgements

OpenVSLAM is based on an indirect SLAM algorithm with sparse features, such as [ORB-SLAM](https://arxiv.org/abs/1502.00956)/[ORB-SLAM2](https://arxiv.org/abs/1610.06475), [ProSLAM](https://arxiv.org/abs/1709.04377), and [UcoSLAM](https://arxiv.org/abs/1902.03729).

The core architecture is based on ORB-SLAM/ORB-SLAM2 and the code has been redesigned and written from scratch to improve scalability, readability, performance, etc.

UcoSLAM has implemented the parallelization of feature extraction, map storage and loading earlier.

ProSLAM has implemented a highly modular and easily understood system earlier.

### Examples

Some code snippets to understand the core functionalities of the system are provided.

You can employ these snippets for in your own programs.

Please see the `*.cc` files in `./example` directory or check [Simple Tutorial](https://stella-cv.readthedocs.io/en/latest/simple_tutorial.html) and [Example](https://stella-cv.readthedocs.io/en/latest/example.html).

## Installation

Please see [**Installation**](https://stella-cv.readthedocs.io/en/latest/installation.html) chapter in the [documentation](https://stella-cv.readthedocs.io/).

[**The instructions for Docker users**](https://stella-cv.readthedocs.io/en/latest/docker.html) are also provided.

## Tutorial

Please see [**Simple Tutorial**](https://stella-cv.readthedocs.io/en/latest/simple_tutorial.html) chapter in the [documentation](https://stella-cv.readthedocs.io/).

A sample ORB vocabulary file can be downloaded from [here](https://github.com/stella-cv/FBoW_orb_vocab/raw/main/orb_vocab.fbow).

Sample datasets are also provided at [here](https://drive.google.com/open?id=1A_gq8LYuENePhNHsuscLZQPhbJJwzAq4).

If you would like to run visual SLAM with standard benchmarking datasets (e.g. KITTI Odometry dataset), please see [**SLAM with standard datasets**](https://stella-cv.readthedocs.io/en/latest/example.html#slam-with-standard-datasets) section in the [documentation](https://stella-cv.readthedocs.io/).

## Community

Please contact us via [GitHub Discussions](https://github.com/stella-cv/stella_vslam/discussions) if you have any questions or notice any bugs about the software.

## Currently working on

- Refactoring

- Algorithm changes and parameter additions to improve performance

- Add tests

- Marker integration

- Implementation of extra camera models

- Python bindings

- IMU integration

The higher up the list, the higher the priority.

Feedbacks, feature requests, and contribution are welcome!

## License

**2-clause BSD license** (see [LICENSE](./LICENSE))

The following files are derived from third-party libraries.

- `./3rd/json` : [nlohmann/json \[v3.6.1\]](https://github.com/nlohmann/json) (MIT license)

- `./3rd/spdlog` : [gabime/spdlog \[v1.3.1\]](https://github.com/gabime/spdlog) (MIT license)

- `./3rd/tinycolormap` : [yuki-koyama/tinycolormap](https://github.com/yuki-koyama/tinycolormap) (MIT license)

- `./3rd/FBoW` : [stella-cv/FBoW](https://github.com/stella-cv/FBoW) (MIT license) (forked from [rmsalinas/fbow](https://github.com/rmsalinas/fbow))

- `./src/stella_vslam/solver/essential_5pt.h` : part of [libmv/libmv](https://github.com/libmv/libmv) (MIT license)

- `./src/stella_vslam/solver/pnp_solver.cc` : part of [laurentkneip/opengv](https://github.com/laurentkneip/opengv) (3-clause BSD license)

- `./src/stella_vslam/feature/orb_extractor.cc` : part of [opencv/opencv](https://github.com/opencv/opencv) (3-clause BSD License)

- `./src/stella_vslam/feature/orb_point_pairs.h` : part of [opencv/opencv](https://github.com/opencv/opencv) (3-clause BSD License)

Please use `g2o` as the dynamic link library because `csparse_extension` module of `g2o` is LGPLv3+.

## Authors of the original version of OpenVSLAM

- Shinya Sumikura ([@shinsumicco](https://github.com/shinsumicco))

- Mikiya Shibuya ([@MikiyaShibuya](https://github.com/MikiyaShibuya))

- Ken Sakurada ([@kensakurada](https://github.com/kensakurada))

## Citation of original version of OpenVSLAM

OpenVSLAM **won first place** at **ACM Multimedia 2019 Open Source Software Competition**.

If OpenVSLAM helps your research, please cite the paper for OpenVSLAM. Here is a BibTeX entry:

```

@inproceedings{openvslam2019,

  author = {Sumikura, Shinya and Shibuya, Mikiya and Sakurada, Ken},

  title = {{OpenVSLAM: A Versatile Visual SLAM Framework}},

  booktitle = {Proceedings of the 27th ACM International Conference on Multimedia},

  series = {MM '19},

  year = {2019},

  isbn = {978-1-4503-6889-6},

  location = {Nice, France},

  pages = {2292--2295},

  numpages = {4},

  url = {http://doi.acm.org/10.1145/3343031.3350539},

  doi = {10.1145/3343031.3350539},

  acmid = {3350539},

  publisher = {ACM},

  address = {New York, NY, USA}

}

```

The preprint can be found [here](https://arxiv.org/abs/1910.01122).

## Reference

- Raúl Mur-Artal, J. M. M. Montiel, and Juan D. Tardós. 2015. ORB-SLAM: a Versatile and Accurate Monocular SLAM System. IEEE Transactions on Robotics 31, 5 (2015), 1147–1163.

- Raúl Mur-Artal and Juan D. Tardós. 2017. ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras. IEEE Transactions on Robotics 33, 5 (2017), 1255–1262.

- Dominik Schlegel, Mirco Colosi, and Giorgio Grisetti. 2018. ProSLAM: Graph SLAM from a Programmer’s Perspective. In Proceedings of IEEE International Conference on Robotics and Automation (ICRA). 1–9.

- Rafael Muñoz-Salinas and Rafael Medina Carnicer. 2019. UcoSLAM: Simultaneous Localization and Mapping by Fusion of KeyPoints and Squared Planar Markers. arXiv:1902.03729.

- Mapillary AB. 2019. OpenSfM. .

- Giorgio Grisetti, Rainer Kümmerle, Cyrill Stachniss, and Wolfram Burgard. 2010. A Tutorial on Graph-Based SLAM. IEEE Transactions on Intelligent Transportation SystemsMagazine 2, 4 (2010), 31–43.

- Rainer Kümmerle, Giorgio Grisetti, Hauke Strasdat, Kurt Konolige, and Wolfram Burgard. 2011. g2o: A general framework for graph optimization. In Proceedings of IEEE International Conference on Robotics and Automation (ICRA). 3607–3613.