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https://github.com/Hardy-Uint/awesome-SLAM-algorithm

SLAM algorithm
https://github.com/Hardy-Uint/awesome-SLAM-algorithm

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SLAM algorithm

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# SLAM



## 优秀开源项目汇总



[https://github.com/OpenSLAM/awesome-SLAM-list](https://github.com/OpenSLAM/awesome-SLAM-list)



[https://github.com/tzutalin/awesome-visual-slam](https://github.com/tzutalin/awesome-visual-slam)



https://github.com/kanster/awesome-slam



https://github.com/YoujieXia/Awesome-SLAM



[Recent_SLAM_Research](https://github.com/YiChenCityU/Recent_SLAM_Research)



[https://github.com/youngguncho/awesome-slam-datasets](https://github.com/youngguncho/awesome-slam-datasets)



[https://github.com/marknabil/SFM-Visual-SLAM](https://github.com/marknabil/SFM-Visual-SLAM)



[https://github.com/ckddls1321/SLAM_Resources](https://github.com/ckddls1321/SLAM_Resources)



## 激光SLAM



> 分为前端和后端。其中前端主要完成匹配和位置估计，后端主要完成进一步的优化约束。

>

> 整个SLAM大概可以分为前端和后端，前端相当于VO（视觉里程计），研究帧与帧之间变换关系。首先提取每帧图像特征点，利用相邻帧图像，进行特征点匹配，然后利用RANSAC去除大噪声，然后进行匹配，得到一个pose信息（位置和姿态），同时可以利用IMU（Inertial measurement unit惯性测量单元）提供的姿态信息进行滤波融合。

>

> 后端则主要是对前端出结果进行优化，利用滤波理论（EKF、UKF、PF）、或者优化理论TORO、G2O进行树或者图的优化。最终得到最优的位姿估计。



### 数据预处理



### 点云匹配



### 地图构建



## 视觉SLAM



### Books



- [视觉SLAM十四讲]() 高翔

- [机器人学中的状态估计]()

- [概率机器人]()

- [Simultaneous Localization and Mapping for Mobile Robots: Introduction and Methods](http://www.igi-global.com/book/simultaneous-localization-mapping-mobile-robots/66380) by Juan-Antonio Fernández-Madrigal and José Luis Blanco Claraco, 2012

- [Simultaneous Localization and Mapping: Exactly Sparse Information Filters ](http://www.worldscientific.com/worldscibooks/10.1142/8145/)by Zhan Wang, Shoudong Huang and Gamini Dissanayake, 2011

- [An Invitation to 3-D Vision -- from Images to Geometric Models](http://vision.ucla.edu/MASKS/) by Yi Ma, Stefano Soatto, Jana Kosecka and Shankar S. Sastry, 2005

- [Multiple View Geometry in Computer Vision](http://www.robots.ox.ac.uk/~vgg/hzbook/) by Richard Hartley and Andrew Zisserman, 2004

- [Numerical Optimization](http://home.agh.edu.pl/~pba/pdfdoc/Numerical_Optimization.pdf) by Jorge Nocedal and Stephen J. Wright, 1999



### Courses&&Lectures



- [SLAM Tutorial@ICRA 2016](http://www.dis.uniroma1.it/~labrococo/tutorial_icra_2016/)

- [Geometry and Beyond - Representations, Physics, and Scene Understanding for Robotics](http://rss16-representations.mit.edu/) at Robotics: Science and Systems (2016)

- [Robotics - UPenn](https://www.coursera.org/specializations/robotics) on Coursera by Vijay Kumar (2016)

- [Robot Mapping - UniFreiburg](http://ais.informatik.uni-freiburg.de/teaching/ws15/mapping/) by Gian Diego Tipaldi and Wolfram Burgard (2015-2016)

- [Robot Mapping - UniBonn](http://www.ipb.uni-bonn.de/robot-mapping/) by Cyrill Stachniss (2016)

- [Introduction to Mobile Robotics - UniFreiburg](http://ais.informatik.uni-freiburg.de/teaching/ss16/robotics/) by Wolfram Burgard, Michael Ruhnke and Bastian Steder (2015-2016)

- [Computer Vision II: Multiple View Geometry - TUM](http://vision.in.tum.de/teaching/ss2016/mvg2016) by Daniel Cremers ( Spring 2016)

- [Advanced Robotics - UCBerkeley](http://www.cs.berkeley.edu/~pabbeel/) by Pieter Abbeel (Fall 2015)

- [Mapping, Localization, and Self-Driving Vehicles](https://www.youtube.com/watch?v=x5CZmlaMNCs) at CMU RI seminar by John Leonard (2015)

- [The Problem of Mobile Sensors: Setting future goals and indicators of progress for SLAM](http://ylatif.github.io/movingsensors/) sponsored by Australian Centre for Robotics and Vision (2015)

- [Robotics - UPenn](https://alliance.seas.upenn.edu/~meam620/wiki/index.php?n=Main.HomePage) by Philip Dames and Kostas Daniilidis (2014)

- [Autonomous Navigation for Flying Robots](http://vision.in.tum.de/teaching/ss2014/autonavx) on EdX by Jurgen Sturm and Daniel Cremers (2014)

- [Robust and Efficient Real-time Mapping for Autonomous Robots](https://www.youtube.com/watch?v=_W3Ua1Yg2fk) at CMU RI seminar by Michael Kaess (2014)

- [KinectFusion - Real-time 3D Reconstruction and Interaction Using a Moving Depth Camera](https://www.youtube.com/watch?v=bRgEdqDiOuQ) by David Kim (2012)



### Code



1. [ORB-SLAM](https://github.com/raulmur/ORB_SLAM)

2. [LSD-SLAM](https://github.com/tum-vision/lsd_slam)

3. [ORB-SLAM2](https://github.com/raulmur/ORB_SLAM2)

4. [DVO: Dense Visual Odometry](https://github.com/tum-vision/dvo_slam)

5. [SVO: Semi-Direct Monocular Visual Odometry](https://github.com/uzh-rpg/rpg_svo)

6. [G2O: General Graph Optimization](https://github.com/RainerKuemmerle/g2o)

7. [RGBD-SLAM](https://github.com/felixendres/rgbdslam_v2)



| Project                                                      | Language | License                    |

| ------------------------------------------------------------ | -------- | -------------------------- |

| [COSLAM](http://drone.sjtu.edu.cn/dpzou/project/coslam.php)  | C++      | GNU General Public License |

| [DSO-Direct Sparse Odometry](https://github.com/JakobEngel/dso) | C++      | GPLv3                      |

| [DTSLAM-Deferred Triangulation SLAM](https://github.com/plumonito/dtslam) | C++      | modified BSD               |

| [LSD-SLAM](https://github.com/tum-vision/lsd_slam/)          | C++/ROS  | GNU General Public License |

| [MAPLAB-ROVIOLI](https://github.com/ethz-asl/maplab)         | C++/ROS  | Apachev2.0                 |

| [OKVIS: Open Keyframe-based Visual-Inertial SLAM](https://github.com/ethz-asl/okvis) | C++      | BSD                        |

| [ORB-SLAM](https://github.com/raulmur/ORB_SLAM2)             | C++      | GPLv3                      |

| [REBVO - Realtime Edge Based Visual Odometry for a Monocular Camera](https://github.com/JuanTarrio/rebvo) | C++      | GNU General Public License |

| [SVO semi-direct Visual Odometry](https://github.com/uzh-rpg/rpg_svo) | C++/ROS  | GNU General Public License |