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https://github.com/qxiaofan/awesome_slam_papers
主要汇总优秀的SLAM Papers 以及优质的开源代码与学习资源
https://github.com/qxiaofan/awesome_slam_papers
List: awesome_slam_papers
Last synced: 16 days ago
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主要汇总优秀的SLAM Papers 以及优质的开源代码与学习资源
- Host: GitHub
- URL: https://github.com/qxiaofan/awesome_slam_papers
- Owner: qxiaofan
- Created: 2020-08-14T03:31:11.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2021-01-27T03:13:10.000Z (almost 4 years ago)
- Last Synced: 2024-11-20T08:02:27.530Z (about 1 month ago)
- Size: 22.5 KB
- Stars: 22
- Watchers: 1
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- ultimate-awesome - awesome_slam_papers - 主要汇总优秀的SLAM Papers 以及优质的开源代码与学习资源. (Other Lists / Monkey C Lists)
README
## 目录
- [一 前言](#前言)
- [二 优秀开源项目汇总](#优秀开源项目汇总)
- [三 激光SLAM](#激光SLAM)
- [四 视觉SLAM从入门到进阶](#视觉SLAM从入门到进阶)
## 一 前言
> 公众号：[3D视觉工坊](https://mp.weixin.qq.com/s?__biz=MzU1MjY4MTA1MQ==&mid=2247484684&idx=1&sn=e812540aee03a4fc54e44d5555ccb843&chksm=fbff2e38cc88a72e180f0f6b0f7b906dd616e7d71fffb9205d529f1238e8ef0f0c5554c27dd7&token=691734513&lang=zh_CN#rd)
>
> 主要关注：3D视觉算法、SLAM、vSLAM、计算机视觉、深度学习、自动驾驶、图像处理以及技术干货分享
>
> 运营者和嘉宾介绍：运营者来自国内一线大厂的算法工程师，深研3D视觉、vSLAM、计算机视觉、点云处理、深度学习、自动驾驶、图像处理、三维重建等领域，特邀嘉宾包括国内外知名高校的博士硕士，旷视、商汤、百度、阿里等就职的算法大佬，欢迎一起交流学习
>
> 小助理微信：CV3Der
>
> 联系邮箱：[email protected]
## 二 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)
## 三 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
## 四 视觉SLAM从入门到进阶
### 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 |