GSLAM (A General SLAM Framework and BenchMark)

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
• Begin with GSLAM Core
  • Svar: A Tiny Modern C++ Header Brings Unified Interface for Different Languages
  • Messenger: A Tiny Class Implemented ROS Like Pub/Sub Messaging.
  • 3D Transformations: SO3, SE3, SIM3
  • Camera Models in GSLAM
  • GImage: Replace cv::Mat with A Tiny Header
  • SLAM Map Data Structure
  • Binary Built-in File Resource
  • PICMake: A CMake Tool to Write More Elegent CMakeList.txt
• Plugins of GSLAM
  • The Plugin Structure of GSLAM
  • Application Plugin for GSLAM
  • Dataset Plugin for GSLAM
  • qviz Plugin for GSLAM
• Develop SLAM with GSLAM
  • Porting ORBSLAM to GSLAM Application
  • Tools for Developing a SLAM
  • SLAM Plugins Implemented
• Evaluate SLAM Plugins with GSLAM
  • Evaluate a SLAM plugin
  • A Dockerized SLAM BenchMark
• Use GSLAM in Other Languages
  • Use GSLAM in Python

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 <dataset_path>/odomentry/color/00/mono.kitti

# Play kitti with stereo mode
gslam qviz play -dataset <dataset_path>/odomentry/color/00/stereo.kitti

# Play TUM RGBD Dataset (associate.txt file prepared)
gslam qviz play -dataset <dataset_path>/rgbd_dataset_freiburg1_360/.tumrgbd

# Play TUM Monocular (images unziped)
gslam qviz play -dataset <dataset_path>/calib_narrowGamma_scene1/.tummono

# Play EuRoC Dataset with IMU frames
gslam qviz play -dataset <dataset_path>/EuRoC/MH_01_easy/mav0/.euroc

# Play NPU DroneMap Dataset
gslam qviz play -dataset <dataset_path>/DroneMap/phantom3-village/phantom3-village-kfs/.npudronemap
gslam qviz play -dataset <dataset_path>/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 <dataset_path>/odomentry/color/00/mono.kitti -playspeed 2.

The following dataset plugins are now implemented:

Name	Channels	Description
KITTI	Stereo,Pose
TUMMono	Monocular
TUMRGBD	RGBD,Pose
EuRoc	IMU,Stereo
NPUDroneMap	GPS,Monocular
CVMono	Monocular	Online camera or video dataset using opencv.

Users can also implement dataset plugins by own.

3.3. Implemented SLAM plugins

Name	ScreenShot	Description
DSO		code
ORBSLAM		code
SVO		code
TheiaSfM		code

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.

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