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awesome-vslam-research's Introduction

本人研究方向是视觉SLAM,针对感兴趣的内容进行总结:

  1. ORB-SLAM2算法: 源码及其改进;
  2. Line-SLAM:paper及其源码;
  3. 经典SLAM算法: 经典、优秀的开源工程 ;
  4. 科研工具 :论文或实验用到的一些工具;
  5. 优秀作者和实验室:自己感兴趣的大佬和实验室;
  6. 学习材料:入门学习SLAM学习资料;
  7. SLAM、三维重建相关资源:常用的 Github 仓库,如何找论文;
  8. SLAM应用和企业:包含SLAM的应用,及其招聘公司的公司(名单可能不完善);
  9. 工作面试:包含SLAM算法面试的时候准备经验;

目录

一、ORB SLAM及其改进

二、line-SLAM

三、经典SLAM系统

四、优秀作者与实验室

五、科研工具

六、学习材料 -

七、SLAM、三维重建相关资源

八、SLAM应用和企业 -

九、工作面经 -

十、主要参考

一、ORB SLAM及其改进

​ paper: ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras

​ code:ORB-SLAM: https://github.com/raulmur/ORB_SLAM . (Monocular. ROS integrated)

​ paper:ORB-SLAM: A Versatile and Accurate Monocular SLAM System

​ code:ORB-SLAM2: https://github.com/raulmur/ORB_SLAM2 . (Monocular, Stereo, RGB-D. ROS optional)

1. 加入点线特征
2. 加入IMU
3. 加入wheel Odometer
4. 改进直接法
5. 加入Odometer和gyro
6. ORB-SLAM2 + 目标检测/分割的方案语义建图
7. 增加鱼眼相机模型
8. 动态环境
9. 其他的改变

二、line-SLAM

  • paper: Yijia H , Ji Z , Yue G , et al. PL-VIO: Tightly-Coupled Monocular Visual–Inertial Odometry Using Point and Line Features[J]. Sensors, 2018, 18(4):1159-.

code: https://github.com/HeYijia/PL-VIO

三、经典SLAM系统

3.1 VSLAM
1. PTAM
2. S-PTAM(双目 PTAM)
3. MonoSLAM
4. ORB-SLAM2

以下5, 6, 7, 8几项是 TUM 计算机视觉组全家桶,官方主页:https://vision.in.tum.de/research/vslam/dso

5. DSO
6. LDSO
7. LSD-SLAM
8. DVO-SLAM
9. SVO
10. openvslam
3.2 VIO
1. msckf_vio
2. rovio
3. R-VIO
4. okvis
5. VIORB
6. VINS-mono
7. VINS-RGBD
8. Open-VINS

四、优秀作者与实验室

4.1 Julian Straub MIT,facebook VR 实验室
4.2 牛津大学 Duncan Frost(PTAM 课题组)
4.3 Yoshikatsu NAKAJIMA (中島 由勝)
4.4 Alejo Concha(苏黎世Oculus VR)
  • 介绍:单目稠密建图,布局平面,超像素,曼哈顿世界
  • 个人主页 谷歌学术 Github
  • 论文:
    • Marta Salas, Wajahat Hussain, Alejo Concha, Luis Montano, Javier Civera, J. M. M. Montiel.Layout Aware Visual Tracking and Mapping (pdf) (video 1) (video 2). IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS15), Hamburg, Germany, 2015.
4.5 波兹南理工大学移动机器人实验室
4.6 Xiaohu Lu
4.7 澳大利亚机器人视觉中心 Yasir Latif
4.8 西班牙马拉加大学博士生:RubénGómezOjeda
4.9法国运输,规划和网络科学与技术研究所博士:Nicolas Antigny
  • 主要研究城市环境下单目 SLAM,基于场景和已知物体的大环境增强现实,传感器融合
  • researchgate YouTube
4.10 三星 AI 实验室(莫斯科):Alexander Vakhitov
4.11 苏黎世联邦理工学院计算机视觉与几何实验室:Prof. Marc Pollefeys
4.12 香港中文大学机器人、感知与 AI 实验室
4.13 微软高级工程师、苏黎世联邦理工:Johannes L. Schönberger
4.14 美国犹他大学计算机学院:Srikumar Ramalingam
4.15 德国马克斯普朗克智能系统研究所:Jörg Stückler
4.16 麻省理工学院航空航天控制实验室
4.17 约翰·霍普金斯大学计算机系博士 Long Qian
4.18 佐治亚理工学院机器人与机器智能研究所
4.19 其他实验室和大牛补充

五、科研工具

图为曹秀洁师姐(北航博士)整理

img

5.1 Linux

学习网站

5.3 Shell

学习资源

  • Shell在线速查表:

https://devhints.io/bash

https://link.zhihu.com/?target=http%3A//www.tldp.org/LDP/abs/html/

5.4 Vim

学习网站

  • OpenVim:

https://link.zhihu.com/?target=http%3A//www.openvim.com/tutorial.html

  • Vim Adventures:

https://link.zhihu.com/?target=http%3A//vim-adventures.com/

  • Vim详细教程:

https://zhuanlan.zhihu.com/p/68111471

  • Interactive Vim tutorial:

https://link.zhihu.com/?target=http%3A//www.openvim.com/

  • 最详细的Vim编辑器指南:

https://www.shiyanlou.com/questions/2721/

  • 简明Vim教程:

https://link.zhihu.com/?target=http%3A//coolshell.cn/articles/5426.html

  • Vim学习资源整理:

https://link.zhihu.com/?target=https%3A//github.com/vim-china/hello-vim

5.5 Cmake
5.6 Git&Github

​ Github:https://github.com/lishuwei0424/Git-Github-Notes

https://git-scm.com/book/zh/v2

  • Github超详细的Git学习资料:

https://link.zhihu.com/?target=https%3A//github.com/xirong/my-git

  • Think like Git:

http://think-like-a-git.net/

​ Atlassian Git Tutorial:

https://link.zhihu.com/?target=https%3A//www.atlassian.com/git/tutorials

  • Git Workflows and Tutorials:

​ 原文: https://www.atlassian.com/git/tutorials/comparing-workflows

​ 译文: https://github.com/xirong/my-git/blob/master/git-workflow-tutorial.md

  • 版本管理工具介绍--Git篇:

https://link.zhihu.com/?target=http%3A//www.imooc.com/learn/208

  • 廖雪峰Git教程:

https://www.liaoxuefeng.com/wiki/896043488029600

5.7 GDB
5.8 pangolin

http://blog.gqylpy.com/gqy/20285/

https://github.com/stevenlovegrove/Pangolin

5.9 轨迹误差分析工具
5.10 C++

xmind for computer science course

1.c++与c语言 2.Linux系统 3.计算机网络 4.计算机组成原理 5.操作系统 6.数据结构 7.编译原理 8.软件设计模式 9.数据库 10.面试刷题

https://github.com/lishuwei0424/xmind_for_cs_basics

六、学习材料

七、SLAM、三维重建相关资源

7.1.常用的GitHub地址

https://github.com/YiChenCityU/Recent_SLAM_Research(跟踪SLAM前沿动态论文,更新的很频繁)

https://github.com/wuxiaolang/Visual_SLAM_Related_Research

https://github.com/tzutalin/awesome-visual-slam

https://github.com/OpenSLAM/awesome-SLAM-list

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

https://github.com/youngguncho/awesome-slam-datasets

https://github.com/openMVG/awesome_3DReconstruction_list

https://github.com/Soietre/awesome_3DReconstruction_list

https://github.com/electech6/owesome-RGBD-SLAM

https://github.com/uzh-rpg/event-based_vision_resources

https://github.com/GeekLiB/Lee-SLAM-source

7.2.找论文图(SLAM研学社)

百度脑图:https://naotu.baidu.com/file/edf7d340203d40d9abc65e59596e0ad5?token=2eafad7bf90fd163 img

八、SLAM应用和企业

​ 8.1 图来之崔大佬整理

img

8.2 图来之六哥整理(公众号:计算机视觉life)

百度脑图: https://naotu.baidu.com/file/1b084951927b765ba0584743bfaa9ac4?token=7f67de64501c0884

img

九、工作面经

十、主要参考

1.https://github.com/wuxiaolang/Visual_SLAM_Related_Research

2.3D视觉工坊: https://mp.weixin.qq.com/s/fQuyxRGG1QbV8URGrmYvUA

3.计算机视觉life   补: img

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