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https://github.com/zdzhaoyong/GSLAM

A General Simultaneous Localization and Mapping Framework which supports feature based or direct method and different sensors including monocular camera, RGB-D sensors or any other input types can be handled.
https://github.com/zdzhaoyong/GSLAM

Last synced: 6 days ago
JSON representation

Host: GitHub
URL: https://github.com/zdzhaoyong/GSLAM
Owner: zdzhaoyong
License: bsd-2-clause
Created: 2016-08-10T13:14:26.000Z (about 8 years ago)
Default Branch: master
Last Pushed: 2020-04-01T15:30:20.000Z (over 4 years ago)
Last Synced: 2024-10-30T04:49:36.209Z (10 days ago)
Language: C++
Size: 7.63 MB
Stars: 1,027
Watchers: 59
Forks: 269
Open Issues: 21
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

Awesome-SLAM - GSLAM (A General SLAM Framework and BenchMark)

README

        # GSLAM (A General SLAM Framework and BenchMark)

[![Build Status](https://travis-ci.org/zdzhaoyong/GSLAM.svg?branch=master)](https://travis-ci.org/zdzhaoyong/GSLAM)

[![License](https://img.shields.io/badge/license-BSD--2--Clause-blue.svg)](./LICENSE)

[![Version](https://img.shields.io/github/release/zdzhaoyong/GSLAM.svg)](https://github.com/zdzhaoyong/GSLAM/releases)

[![](https://github.com/zdzhaoyong/GSLAM/raw/resource/gslam_iccv_poster.png)](https://github.com/zdzhaoyong/GSLAM/raw/resource/gslam_iccv_poster.png)

## 1. Introduction

If you use this code for your research, please cite our paper GSLAM: A General SLAM Framework and Benchmark:

```

@inproceedings{gslamICCV2019,

	title={GSLAM: A General SLAM Framework and Benchmark},

	author={Yong Zhao, Shibiao Xu, Shuhui Bu, Hongkai Jiang, Pengcheng Han},

	booktitle={Proceedings of the IEEE International Conference on Computer Vision},

	year={2019},

	organization={IEEE}

}

```

```

@article{gslam2019,

	title={GSLAM: A General SLAM Framework and Benchmark},

	author={Yong Zhao, Shibiao Xu, Shuhui Bu, Hongkai Jiang, Pengcheng Han},

	journal={arXiv:1902.07995 },

	year={2019}

}

```

### 1.1. What is GSLAM?

1. *For SLAM developers* : Everyone can develop their own SLAM implementation based on GSLAM and publish it as a plugin with open-source or not. 

2. *For SLAM users* : Applications are able to use different SLAM plugins with the same API without recompilation and implementations are loaded at runtime.

## 2. Documentation

Documentation: https://zdzhaoyong.github.io/GSLAM/

- [Compile and Install](https://zdzhaoyong.github.io/GSLAM/compile.html)

- **Begin with GSLAM Core**

  - [Svar: A Tiny Modern C++ Header Brings Unified Interface for Different Languages](https://zdzhaoyong.github.io/GSLAM/svar.html)

  - [Messenger: A Tiny Class Implemented ROS Like Pub/Sub Messaging.](https://zdzhaoyong.github.io/GSLAM/messenger.html)

  - [3D Transformations: SO3, SE3, SIM3](https://zdzhaoyong.github.io/GSLAM/transform.html)

  - [Camera Models in GSLAM](https://zdzhaoyong.github.io/GSLAM/camera.html)

  - [GImage: Replace cv::Mat with A Tiny Header](https://zdzhaoyong.github.io/GSLAM/gimage.html)

  - [SLAM Map Data Structure](https://zdzhaoyong.github.io/GSLAM/map.html)

  - [Binary Built-in File Resource](https://zdzhaoyong.github.io/GSLAM/resource.html)

  - [PICMake: A CMake Tool to Write More Elegent CMakeList.txt](https://zdzhaoyong.github.io/GSLAM/picmake.html)

- **Plugins of GSLAM**

  - [The Plugin Structure of GSLAM](https://zdzhaoyong.github.io/GSLAM/registry.html)

  - [Application Plugin for GSLAM](https://zdzhaoyong.github.io/GSLAM/application.html)

  - [Dataset Plugin for GSLAM](https://zdzhaoyong.github.io/GSLAM/dataset.html)

  - [qviz Plugin for GSLAM](https://zdzhaoyong.github.io/GSLAM/qviz.html)

- **Develop SLAM with GSLAM**

  - [Porting ORBSLAM to GSLAM Application](https://zdzhaoyong.github.io/GSLAM/orbslam.html)

  - [Tools for Developing a SLAM](https://zdzhaoyong.github.io/GSLAM/tools.html)

  - [SLAM Plugins Implemented](https://zdzhaoyong.github.io/GSLAM/slams.html)

- **Evaluate SLAM Plugins with GSLAM**

  - [Evaluate a SLAM plugin](https://zdzhaoyong.github.io/GSLAM/evaluate.html)

  - [A Dockerized SLAM BenchMark](https://zdzhaoyong.github.io/GSLAM/docker.html)

- **Use GSLAM in Other Languages**

  - [Use GSLAM in Python](https://zdzhaoyong.github.io/GSLAM/python.html)

## 3. Start with GSLAM

## 3.1. Basic usages

Test modules with google test:

```

gslam tests --gtest_filter=*

```

Run a slam system with datasets:

```

gslam qviz orbslam play -dataset dataset_path

```

## 3.2. Supported Datasets

GSLAM now implemented serveral plugins for public available datasets. It is very easy to play different datasets with parameter "Dataset" setted:

```

# Play kitti with monocular mode

gslam qviz play -dataset /odomentry/color/00/mono.kitti

# Play kitti with stereo mode

gslam qviz play -dataset /odomentry/color/00/stereo.kitti

# Play TUM RGBD Dataset (associate.txt file prepared)

gslam qviz play -dataset /rgbd_dataset_freiburg1_360/.tumrgbd

# Play TUM Monocular (images unziped)

gslam qviz play -dataset /calib_narrowGamma_scene1/.tummono

# Play EuRoC Dataset with IMU frames

gslam qviz play -dataset /EuRoC/MH_01_easy/mav0/.euroc

# Play NPU DroneMap Dataset

gslam qviz play -dataset /DroneMap/phantom3-village/phantom3-village-kfs/.npudronemap

gslam qviz play -dataset /DroneMap/phantom3-village/phantom3-village-unified/.npudronemap

```

The datasets are default to be played on realtime, and the play speed can be controled with "playspeed":

```

gslam qviz play -dataset /odomentry/color/00/mono.kitti -playspeed 2.

```

The following dataset plugins are now implemented:

| Name    |    Channels        |   Description    |

| ------- |:------------------:|:-------------:|

| [KITTI](http://www.cvlibs.net/datasets/kitti/)   | Stereo,Pose        |               |

| [TUMMono](https://vision.in.tum.de/data/datasets/mono-dataset) | Monocular          | |

| [TUMRGBD](https://vision.in.tum.de/data/datasets/rgbd-dataset) | RGBD,Pose          ||

| [EuRoc](https://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets)   | IMU,Stereo         ||

| [NPUDroneMap](http://zhaoyong.adv-ci.com/downloads/npu-dronemap-dataset/)| GPS,Monocular   ||

| CVMono | Monocular           | Online camera or video dataset using opencv.|

Users can also [implement dataset plugins by own](https://zdzhaoyong.github.io/GSLAM/dataset.html).

### 3.3. Implemented SLAM plugins

| Name        |  ScreenShot  | Description  |

| ------- |:------:|:-------------:|

| [DSO](https://github.com/JakobEngel/dso)     |   | [code](https://github.com/pi-gslam/gslam_dso) |

| [ORBSLAM](https://github.com/raulmur/ORB_SLAM) |  | [code](https://github.com/pi-gslam/gslam_orbslam) |

| [SVO](https://github.com/uzh-rpg/rpg_svo) |  | [code](https://github.com/pi-gslam/gslam_svo) |

| [TheiaSfM](http://www.theia-sfm.org/) |  | [code](https://github.com/zdzhaoyong/gslam_theia) |

## 4. Contacts

YongZhao: [email protected]

ShuhuiBu: [email protected]

ShibiaoXu: [email protected]

## 5. License

The GSLAM library is licensed under the BSD license. Note that this text refers only to the license for GSLAM itself, independent of its optional dependencies, which are separately licensed. Building GSLAM with these optional dependencies may affect the resulting GSLAM license.

```

Copyright (c) 2018 Northwestern Polytechnical University, Yong Zhao. All rights reserved.

This software was developed by the Yong Zhao at Northwestern Polytechnical University.

All advertising materials mentioning features or use of this software must display

the following acknowledgement: This product includes software developed by Northwestern Polytechnical University, PILAB.

Redistribution and use in source and binary forms, with or without modification,

are permitted provided that the following conditions are met:

1. Redistributions of source code must retain the above copyright notice, this list

of conditions and the following disclaimer.

2. Redistributions in binary form must reproduce the above copyright notice, this

list of conditions and the following disclaimer in the documentation and/or

other materials provided with the distribution.

3. All advertising materials mentioning features or use of this software must

display the following acknowledgement: This product includes software developed

by Northwestern Polytechnical University and its contributors.

4. Neither the name of the University nor the names of its contributors may be

used to endorse or promote products derived from this software without specific

prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS

"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT

LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR

A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR

CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,

EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,

PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR

PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF

LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING

NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS

SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

```