前言：

最近在了解一些视觉SLAM算法，发现这篇文章总结的很不错，所以给需要的朋友的也分享一下，感谢前辈的总结。

作者：小吴同学@知乎

整理：羽林君

模块介绍：

1. 本文整理自我的 Github 仓库（包括开源 SLAM 方案，近期论文）：

Visual_SLAM_Related_Researchgithub.com

2.本文简单将各种方案分为以下 7 类（固然有不少文章无法恰当分类，比如动态语义稠密建图的 VISLAM +_+）：

一、Geometric SLAM
二、Semantic / Deep SLAM
三、Multi-Landmarks / Object SLAM
四、Sensor Fusion
五、Dynamic SLAM
六、Mapping
七、Optimization

3. 由于此仓库自 2019 年 3 月开始整理（20 年 3 月公开），所以以下的代码除了经典的框架之外基本都集中在 19 年之后；此外个人比较关注 VO、物体级 SLAM 和多路标 SLAM，所以以下内容收集也不完整，无法涵盖视觉 SLAM 的所有研究，也欢迎大家有好的方案可以在 issue 或评论中分享补充。

一、Geometric SLAM（23 项）

这一类是传统的基于特征点、直接法或半直接法的几何 SLAM。

1. PTAM

论文：Klein G, Murray D. Parallel tracking and mapping for small AR workspaces[C]//Mixed and Augmented Reality, 2007. ISMAR 2007. 6th IEEE and ACM International Symposium on. IEEE, 2007: 225-234.
代码：github.com/Oxford-PTAM/
工程地址：robots.ox.ac.uk/~gk/PTA
作者其他研究：robots.ox.ac.uk/~gk/pub

2. S-PTAM（双目 PTAM）

论文：Taihú Pire,Thomas Fischer, Gastón Castro, Pablo De Cristóforis, Javier Civera and Julio Jacobo Berlles. S-PTAM: Stereo Parallel Tracking and Mapping. Robotics and Autonomous Systems, 2017.
代码：github.com/lrse/sptam
作者其他论文：Castro G, Nitsche M A, Pire T, et al. Efficient on-board Stereo SLAM through constrained-covisibility strategies[J]. Robotics and Autonomous Systems, 2019.

3. MonoSLAM

论文：Davison A J, Reid I D, Molton N D, et al. MonoSLAM: Real-time single camera SLAM[J]. IEEE transactions on pattern analysis and machine intelligence, 2007, 29(6): 1052-1067.
代码：github.com/hanmekim/Sce

4. ORB-SLAM2

论文：Mur-Artal R, Tardós J D. Orb-slam2: An open-source slam system for monocular, stereo, and rgb-d cameras[J]. IEEE Transactions on Robotics, 2017, 33(5): 1255-1262.
代码：github.com/raulmur/ORB_
作者其他论文：

单目半稠密建图：Mur-Artal R, Tardós J D. Probabilistic Semi-Dense Mapping from Highly Accurate Feature-Based Monocular SLAM[C]//Robotics: Science and Systems. 2015, 2015.
VIORB：Mur-Artal R, Tardós J D. Visual-inertial monocular SLAM with map reuse[J]. IEEE Robotics and Automation Letters, 2017, 2(2): 796-803.
多地图：Elvira R, Tardós J D, Montiel J M M. ORBSLAM-Atlas: a robust and accurate multi-map system[J]. arXiv preprint arXiv:1908.11585, 2019.

以下 5, 6, 7, 8 几项是 TUM 计算机视觉组全家桶，官方主页

5. DSO

论文：Engel J, Koltun V, Cremers D. Direct sparse odometry[J]. IEEE transactions on pattern analysis and machine intelligence, 2017, 40(3): 611-625.
代码：github.com/JakobEngel/d
双目 DSO：Wang R, Schworer M, Cremers D. Stereo DSO: Large-scale direct sparse visual odometry with stereo cameras[C]//Proceedings of the IEEE International Conference on Computer Vision. 2017: 3903-3911.
VI-DSO：Von Stumberg L, Usenko V, Cremers D. Direct sparse visual-inertial odometry using dynamic marginalization[C]//2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2018: 2510-2517.

6. LDSO

高翔在 DSO 上添加闭环的工作
论文：Gao X, Wang R, Demmel N, et al. LDSO: Direct sparse odometry with loop closure[C]//2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2018: 2198-2204.
代码：github.com/tum-vision/L

7. LSD-SLAM

论文：Engel J, Schöps T, Cremers D. LSD-SLAM: Large-scale direct monocular SLAM[C]//European conference on computer vision. Springer, Cham, 2014: 834-849.
代码：github.com/tum-vision/l

8. DVO-SLAM

论文：Kerl C, Sturm J, Cremers D. Dense visual SLAM for RGB-D cameras[C]//2013 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 2013: 2100-2106.
代码 1：github.com/tum-vision/d
代码 2：github.com/tum-vision/d
其他论文：

Kerl C, Sturm J, Cremers D. Robust odometry estimation for RGB-D cameras[C]//2013 IEEE international conference on robotics and automation. IEEE, 2013: 3748-3754.
Steinbrücker F, Sturm J, Cremers D. Real-time visual odometry from dense RGB-D images[C]//2011 IEEE international conference on computer vision workshops (ICCV Workshops). IEEE, 2011: 719-722.

9. SVO

苏黎世大学机器人与感知课题组
论文：Forster C, Pizzoli M, Scaramuzza D. SVO: Fast semi-direct monocular visual odometry[C]//2014 IEEE international conference on robotics and automation (ICRA). IEEE, 2014: 15-22.
代码：github.com/uzh-rpg/rpg_
Forster C, Zhang Z, Gassner M, et al. SVO: Semidirect visual odometry for monocular and multicamera systems[J]. IEEE Transactions on Robotics, 2016, 33(2): 249-265.

10. DSM

论文：Zubizarreta J, Aguinaga I, Montiel J M M. Direct sparse mapping[J]. arXiv preprint arXiv:1904.06577, 2019.
代码：github.com/jzubizarreta ；Video

11. openvslam

论文：Sumikura S, Shibuya M, Sakurada K. OpenVSLAM: A Versatile Visual SLAM Framework[C]//Proceedings of the 27th ACM International Conference on Multimedia. 2019: 2292-2295.
代码：github.com/xdspacelab/o ；文档

12. se2lam（地面车辆位姿估计的视觉里程计）

论文：Zheng F, Liu Y H. Visual-Odometric Localization and Mapping for Ground Vehicles Using SE (2)-XYZ Constraints[C]//2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019: 3556-3562.
代码：github.com/izhengfan/se
作者的另外一项工作

论文：Zheng F, Tang H, Liu Y H. Odometry-vision-based ground vehicle motion estimation with se (2)-constrained se (3) poses[J]. IEEE transactions on cybernetics, 2018, 49(7): 2652-2663.
代码：github.com/izhengfan/se

13. GraphSfM（基于图的并行大规模 SFM）

论文：Chen Y, Shen S, Chen Y, et al. Graph-Based Parallel Large Scale Structure from Motion[J]. arXiv preprint arXiv:1912.10659, 2019.
代码：github.com/AIBluefisher

14. LCSD_SLAM（松耦合的半直接法单目 SLAM）

论文：Lee S H, Civera J. Loosely-Coupled semi-direct monocular SLAM[J]. IEEE Robotics and Automation Letters, 2018, 4(2): 399-406.
代码：github.com/sunghoon031/ ；谷歌学术；演示视频
作者另外一篇关于单目尺度的文章代码开源：Lee S H, de Croon G. Stability-based scale estimation for monocular SLAM[J]. IEEE Robotics and Automation Letters, 2018, 3(2): 780-787.

15. RESLAM（基于边的 SLAM）

论文：Schenk F, Fraundorfer F. RESLAM: A real-time robust edge-based SLAM system[C]//2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019: 154-160.
代码：github.com/fabianschenk ；项目主页

16. scale_optimization（将单目 DSO 拓展到双目）

论文：Mo J, Sattar J. Extending Monocular Visual Odometry to Stereo Camera System by Scale Optimization[C]. International Conference on Intelligent Robots and Systems (IROS), 2019.
代码：github.com/jiawei-mo/sc

17. BAD-SLAM（直接法 RGB-D SLAM）

论文：Schops T, Sattler T, Pollefeys M. BAD SLAM: Bundle Adjusted Direct RGB-D SLAM[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019: 134-144.
代码：github.com/ETH3D/badsla

18. GSLAM（集成 ORB-SLAM2，DSO，SVO 的通用框架）

论文：Zhao Y, Xu S, Bu S, et al. GSLAM: A general SLAM framework and benchmark[C]//Proceedings of the IEEE International Conference on Computer Vision. 2019: 1110-1120.
代码：github.com/zdzhaoyong/G

19. ARM-VO（运行于 ARM 处理器上的单目 VO）

论文：Nejad Z Z, Ahmadabadian A H. ARM-VO: an efficient monocular visual odometry for ground vehicles on ARM CPUs[J]. Machine Vision and Applications, 2019: 1-10.
代码：github.com/zanazakaryai

20. cvo-rgbd（直接法 RGB-D VO）

论文：Ghaffari M, Clark W, Bloch A, et al. Continuous Direct Sparse Visual Odometry from RGB-D Images[J]. arXiv preprint arXiv:1904.02266, 2019.
代码：github.com/MaaniGhaffar

21. Map2DFusion（单目 SLAM 无人机图像拼接）

论文：Bu S, Zhao Y, Wan G, et al. Map2DFusion: Real-time incremental UAV image mosaicing based on monocular slam[C]//2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2016: 4564-4571.
代码：github.com/zdzhaoyong/M

22. CCM-SLAM（多机器人协同单目 SLAM）

论文：Schmuck P, Chli M. CCM‐SLAM: Robust and efficient centralized collaborative monocular simultaneous localization and mapping for robotic teams[J]. Journal of Field Robotics, 2019, 36(4): 763-781.
代码：github.com/VIS4ROB-lab/ | Video

23. ORB-SLAM3

论文：Carlos Campos, Richard Elvira, et al.ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM[J]. arXiv preprint arXiv:2007.11898, 2020.
代码：github.com/UZ-SLAMLab/O | Video

二、Semantic / Depp SLAM（16 项）

SLAM 与深度学习相结合的工作当前主要体现在两个方面，一方面是将语义信息参与到建图、位姿估计等环节中，另一方面是端到端地完成 SLAM 的某一个步骤（比如 VO，闭环等）。个人对后者没太关注，也同样欢迎大家在 issue 中分享。

24. MsakFusion

论文：Runz M, Buffier M, Agapito L. Maskfusion: Real-time recognition, tracking and reconstruction of multiple moving objects[C]//2018 IEEE International Symposium on Mixed and Augmented Reality (ISMAR). IEEE, 2018: 10-20.
代码：github.com/martinruenz/

25. SemanticFusion

论文：McCormac J, Handa A, Davison A, et al. Semanticfusion: Dense 3d semantic mapping with convolutional neural networks[C]//2017 IEEE International Conference on Robotics and automation (ICRA). IEEE, 2017: 4628-4635.
代码：github.com/seaun163/sem

26. semantic_3d_mapping

论文：Yang S, Huang Y, Scherer S. Semantic 3D occupancy mapping through efficient high order CRFs[C]//2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2017: 590-597.
代码：github.com/shichaoy/sem

27. Kimera（实时度量与语义定位建图开源库）

论文：Rosinol A, Abate M, Chang Y, et al. Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping[J]. arXiv preprint arXiv:1910.02490, 2019.
代码：github.com/MIT-SPARK/Ki ；演示视频

28. NeuroSLAM（脑启发式 SLAM）

论文：Yu F, Shang J, Hu Y, et al. NeuroSLAM: a brain-inspired SLAM system for 3D environments[J]. Biological Cybernetics, 2019: 1-31.
代码：github.com/cognav/Neuro
第四作者就是 Rat SLAM 的作者，文章也比较了十余种脑启发式的 SLAM

29. gradSLAM（自动分区的稠密 SLAM）

论文：Jatavallabhula K M, Iyer G, Paull L. gradSLAM: Dense SLAM meets Automatic Differentiation[J]. arXiv preprint arXiv:1910.10672, 2019.
代码（预计 20 年 4 月放出）：github.com/montrealrobo ；项目主页，演示视频

30. ORB-SLAM2 + 目标检测/分割的方案语义建图

github.com/floatlazer/s
github.com/qixuxiang/or
github.com/Ewenwan/ORB_

31. SIVO（语义辅助特征选择）

论文：Ganti P, Waslander S. Network Uncertainty Informed Semantic Feature Selection for Visual SLAM[C]//2019 16th Conference on Computer and Robot Vision (CRV). IEEE, 2019: 121-128.
代码：github.com/navganti/SIV

32. FILD（临近图增量式闭环检测）

论文：Shan An, Guangfu Che, Fangru Zhou, Xianglong Liu, Xin Ma, Yu Chen.Fast and Incremental Loop Closure Detection using Proximity Graphs. pp. 378-385, The 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS2019)
代码：github.com/AnshanTJU/FI

33. object-detection-sptam（目标检测与双目 SLAM）

论文：Pire T, Corti J, Grinblat G. Online Object Detection and Localization on Stereo Visual SLAM System[J]. Journal of Intelligent & Robotic Systems, 2019: 1-10.
代码：github.com/CIFASIS/obje

34. Map Slammer（单目深度估计 + SLAM）

论文：Torres-Camara J M, Escalona F, Gomez-Donoso F, et al. Map Slammer: Densifying Scattered KSLAM 3D Maps with Estimated Depth[C]//Iberian Robotics conference. Springer, Cham, 2019: 563-574.
代码：github.com/jmtc7/mapSla

35. NOLBO（变分模型的概率 SLAM）

论文：Yu H, Lee B. Not Only Look But Observe: Variational Observation Model of Scene-Level 3D Multi-Object Understanding for Probabilistic SLAM[J]. arXiv preprint arXiv:1907.09760, 2019.
代码：github.com/bogus2000/NO

36. GCNv2_SLAM （基于图卷积神经网络 SLAM）

论文：Tang J, Ericson L, Folkesson J, et al. GCNv2: Efficient correspondence prediction for real-time SLAM[J]. IEEE Robotics and Automation Letters, 2019, 4(4): 3505-3512.
代码：github.com/jiexiong2016Video

37. semantic_suma（激光语义建图）

论文：Chen X, Milioto A, Palazzolo E, et al. SuMa++: Efficient LiDAR-based semantic SLAM[C]//2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2019: 4530-4537.
代码：github.com/PRBonn/seman ；Video

38. Neural-SLAM（主动神经 SLAM）

论文：Chaplot D S, Gandhi D, Gupta S, et al. Learning to explore using active neural slam[C]. ICLR 2020.
代码：github.com/devendrachap

39. TartanVO：一种通用的基于学习的 VO

论文：Wang W, Hu Y, Scherer S. TartanVO: A Generalizable Learning-based VO[J]. arXiv preprint arXiv:2011.00359, 2020.
代码：github.com/castacks/tar
数据集：IROS2020 TartanAir: A Dataset to Push the Limits of Visual SLAM，数据集地址

三、Multi-Landmarks / Object SLAM（15 项）

其实多路标的点、线、平面 SLAM 和物体级 SLAM 完全可以分类在 Geometric SLAM 和 Semantic SLAM 中，但个人对这一方向比较感兴趣（也是我的研究生课题），所以将其独立出来，开源方案相对较少，但很有意思。

40. PL-SVO（点线 SVO）

论文：Gomez-Ojeda R, Briales J, Gonzalez-Jimenez J. PL-SVO: Semi-direct Monocular Visual Odometry by combining points and line segments[C]//Intelligent Robots and Systems (IROS), 2016 IEEE/RSJ International Conference on. IEEE, 2016: 4211-4216.
代码：github.com/rubengooj/pl

41. stvo-pl（双目点线 VO）

论文：Gomez-Ojeda R, Gonzalez-Jimenez J. Robust stereo visual odometry through a probabilistic combination of points and line segments[C]//2016 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2016: 2521-2526.
代码：github.com/rubengooj/st

42. PL-SLAM（点线 SLAM）

论文：Gomez-Ojeda R, Zuñiga-Noël D, Moreno F A, et al. PL-SLAM: a Stereo SLAM System through the Combination of Points and Line Segments[J]. arXiv preprint arXiv:1705.09479, 2017.
代码：github.com/rubengooj/pl
Gomez-Ojeda R, Moreno F A, Zuñiga-Noël D, et al. PL-SLAM: a stereo SLAM system through the combination of points and line segments[J]. IEEE Transactions on Robotics, 2019, 35(3): 734-746.

43. PL-VIO

论文：He Y, Zhao J, Guo Y, et al. PL-VIO: Tightly-coupled monocular visual–inertial odometry using point and line features[J]. Sensors, 2018, 18(4): 1159.
代码：github.com/HeYijia/PL-V
VINS + 线段：github.com/Jichao-Peng/

44. lld-slam（用于 SLAM 的可学习型线段描述符）

论文：Vakhitov A, Lempitsky V. Learnable line segment descriptor for visual SLAM[J]. IEEE Access, 2019, 7: 39923-39934.
代码：github.com/alexandervak ；Video

点线结合的工作还有很多，国内的比如
+ 上交邹丹平老师的 Zou D, Wu Y, Pei L, et al. StructVIO: visual-inertial odometry with structural regularity of man-made environments[J]. IEEE Transactions on Robotics, 2019, 35(4): 999-1013.
+ 浙大的 Zuo X, Xie X, Liu Y, et al. Robust visual SLAM with point and line features[C]//2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2017: 1775-1782.

45. PlaneSLAM

论文：Wietrzykowski J. On the representation of planes for efficient graph-based slam with high-level features[J]. Journal of Automation Mobile Robotics and Intelligent Systems, 2016, 10.
代码：github.com/LRMPUT/Plane
作者另外一项开源代码，没有找到对应的论文：github.com/LRMPUT/PUTSL

46. Eigen-Factors（特征因子平面对齐）

论文：Ferrer G. Eigen-Factors: Plane Estimation for Multi-Frame and Time-Continuous Point Cloud Alignment[C]//2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2019: 1278-1284.
代码：gitlab.com/gferrer/eige ；演示视频

47. PlaneLoc

论文：Wietrzykowski J, Skrzypczyński P. PlaneLoc: Probabilistic global localization in 3-D using local planar features[J]. Robotics and Autonomous Systems, 2019, 113: 160-173.
代码：github.com/LRMPUT/Plane

48. Pop-up SLAM

论文：Yang S, Song Y, Kaess M, et al. Pop-up slam: Semantic monocular plane slam for low-texture environments[C]//2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2016: 1222-1229.
代码：github.com/shichaoy/pop

49. Object SLAM

论文：Mu B, Liu S Y, Paull L, et al. Slam with objects using a nonparametric pose graph[C]//2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2016: 4602-4609.
代码：github.com/BeipengMu/ob ；Video

50. voxblox-plusplus（物体级体素建图）

论文：Grinvald M, Furrer F, Novkovic T, et al. Volumetric instance-aware semantic mapping and 3D object discovery[J]. IEEE Robotics and Automation Letters, 2019, 4(3): 3037-3044.
代码：github.com/ethz-asl/vox

51. Cube SLAM

论文：Yang S, Scherer S. Cubeslam: Monocular 3-d object slam[J]. IEEE Transactions on Robotics, 2019, 35(4): 925-938.
代码：github.com/shichaoy/cub
对，这就是带我入坑的一项工作，2018 年 11 月份看到这篇论文（当时是预印版）之后开始学习物体级 SLAM，个人对 Cube SLAM 的一些注释和总结：链接。
也有很多有意思的但没开源的物体级 SLAM

Ok K, Liu K, Frey K, et al. Robust Object-based SLAM for High-speed Autonomous Navigation[C]//2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019: 669-675.
Li J, Meger D, Dudek G. Semantic Mapping for View-Invariant Relocalization[C]//2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019: 7108-7115.
Nicholson L, Milford M, Sünderhauf N. Quadricslam: Dual quadrics from object detections as landmarks in object-oriented slam[J]. IEEE Robotics and Automation Letters, 2018, 4(1): 1-8.

52. VPS-SLAM（平面语义 SLAM）

论文：Bavle H, De La Puente P, How J, et al. VPS-SLAM: Visual Planar Semantic SLAM for Aerial Robotic Systems[J]. IEEE Access, 2020.
代码：bitbucket.org/hridaybav

53. Structure-SLAM-PointLine （室内环境单目点线 SLAM）

论文：Li Y, Brasch N, Wang Y, et al. Structure-SLAM: Low-Drift Monocular SLAM in Indoor Environments[J]. IEEE Robotics and Automation Letters, 2020, 5(4): 6583-6590.
代码：github.com/yanyan-li/St

54. PL-VINS

论文：Fu Q, Wang J, Yu H, et al. PL-VINS: Real-Time Monocular Visual-Inertial SLAM with Point and Line[J]. arXiv preprint arXiv:2009.07462, 2020.
代码：github.com/cnqiangfu/PL

四、Sensor Fusion（19 项）

在传感器融合方面只关注了视觉 + 惯导，其他传感器像 LiDAR，GPS 关注较少。

55. msckf_vio

论文：Sun K, Mohta K, Pfrommer B, et al. Robust stereo visual inertial odometry for fast autonomous flight[J]. IEEE Robotics and Automation Letters, 2018, 3(2): 965-972.
代码：github.com/KumarRobotic ；Video

56. rovio

论文：Bloesch M, Omari S, Hutter M, et al. Robust visual inertial odometry using a direct EKF-based approach[C]//2015 IEEE/RSJ international conference on intelligent robots and systems (IROS). IEEE, 2015: 298-304.
代码：github.com/ethz-asl/rov ；Video

57. R-VIO

论文：Huai Z, Huang G. Robocentric visual-inertial odometry[C]//2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2018: 6319-6326.
代码：github.com/rpng/R-VIO ；Video

58. okvis

论文：Leutenegger S, Lynen S, Bosse M, et al. Keyframe-based visual–inertial odometry using nonlinear optimization[J]. The International Journal of Robotics Research, 2015, 34(3): 314-334.
代码：github.com/ethz-asl/okv

59. VIORB

论文：Mur-Artal R, Tardós J D. Visual-inertial monocular SLAM with map reuse[J]. IEEE Robotics and Automation Letters, 2017, 2(2): 796-803.
代码：github.com/jingpang/Lea （VIORB 本身是没有开源的，这是王京大佬复现的一个版本）
VI-ORB-SLAM2：github.com/YoujieXia/VI

60. VINS-mono

论文：Qin T, Li P, Shen S. Vins-mono: A robust and versatile monocular visual-inertial state estimator[J]. IEEE Transactions on Robotics, 2018, 34(4): 1004-1020.
代码：github.com/HKUST-Aerial
双目版 VINS-Fusion：github.com/HKUST-Aerial
移动段 VINS-mobile：github.com/HKUST-Aerial

61. VINS-RGBD

论文：Shan Z, Li R, Schwertfeger S. RGBD-Inertial Trajectory Estimation and Mapping for Ground Robots[J]. Sensors, 2019, 19(10): 2251.
代码：github.com/STAR-Center/ ；Video

62. Open-VINS

论文：Geneva P, Eckenhoff K, Lee W, et al. Openvins: A research platform for visual-inertial estimation[C]//IROS 2019 Workshop on Visual-Inertial Navigation: Challenges and Applications, Macau, China. IROS 2019.
代码：github.com/rpng/open_vi

63. versavis（多功能的视惯传感器系统）

论文：Tschopp F, Riner M, Fehr M, et al. VersaVIS—An Open Versatile Multi-Camera Visual-Inertial Sensor Suite[J]. Sensors, 2020, 20(5): 1439.
代码：github.com/ethz-asl/ver

64. CPI（视惯融合的封闭式预积分）

论文：Eckenhoff K, Geneva P, Huang G. Closed-form preintegration methods for graph-based visual–inertial navigation[J]. The International Journal of Robotics Research, 2018.
代码：github.com/rpng/cpi ；Video

65. TUM Basalt

论文：Usenko V, Demmel N, Schubert D, et al. Visual-inertial mapping with non-linear factor recovery[J]. IEEE Robotics and Automation Letters, 2019.
代码：github.com/VladyslavUse ；Video；Project Page

66. Limo：激光单目视觉里程计

论文：Graeter J, Wilczynski A, Lauer M. Limo: Lidar-monocular visual odometry[C]//2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2018: 7872-7879.
代码：github.com/johannes-gra ；Video

67. LARVIO（多状态约束卡尔曼滤波的单目 VIO）

论文：Qiu X, Zhang H, Fu W, et al. Monocular Visual-Inertial Odometry with an Unbiased Linear System Model and Robust Feature Tracking Front-End[J]. Sensors, 2019, 19(8): 1941.
代码：github.com/PetWorm/LARV
北航邱笑晨博士的一项工作

68. vig-init（垂直边缘加速视惯初始化）

论文：Li J, Bao H, Zhang G. Rapid and Robust Monocular Visual-Inertial Initialization with Gravity Estimation via Vertical Edges[C]//2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2019: 6230-6236.
代码：github.com/zju3dv/vig-i

69. vilib（VIO 前端库）

论文：Nagy B, Foehn P, Scaramuzza D. Faster than FAST: GPU-Accelerated Frontend for High-Speed VIO[J]. arXiv preprint arXiv:2003.13493, 2020.
代码：github.com/uzh-rpg/vili

70. Kimera-VIO

论文：A. Rosinol, M. Abate, Y. Chang, L. Carlone, Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping. IEEE Intl. Conf. on Robotics and Automation (ICRA), 2020.
代码：github.com/MIT-SPARK/Ki

71. maplab（视惯建图框架）

论文：Schneider T, Dymczyk M, Fehr M, et al. maplab: An open framework for research in visual-inertial mapping and localization[J]. IEEE Robotics and Automation Letters, 2018, 3(3): 1418-1425.
代码：github.com/ethz-asl/map
多会话建图，地图合并，视觉惯性批处理优化和闭环

72. lili-om：固态雷达惯性里程计与建图

论文：Li K, Li M, Hanebeck U D. Towards high-performance solid-state-lidar-inertial odometry and mapping[J]. arXiv preprint arXiv:2010.13150, 2020.
代码：github.com/KIT-ISAS/lil

73. CamVox：Lidar辅助视觉 SLAM

论文：ZHU, Yuewen, et al. CamVox: A Low-cost and Accurate Lidar-assisted Visual SLAM System. arXiv preprint arXiv:2011.11357, 2020.
代码：github.com/ISEE-Technol

五、Dynamic SLAM（8 项）

动态 SLAM 也是一个很值得研究的话题，这里不太好分类，很多工作用到了语义信息或者用来三维重建，收集的方案相对较少，欢迎补充 issue。

74. DynamicSemanticMapping（动态语义建图）

论文：Kochanov D, Ošep A, Stückler J, et al. Scene flow propagation for semantic mapping and object discovery in dynamic street scenes[C]//Intelligent Robots and Systems (IROS), 2016 IEEE/RSJ International Conference on. IEEE, 2016: 1785-1792.
代码：github.com/ganlumomo/Dy ；wiki

75. DS-SLAM（动态语义 SLAM）

论文：Yu C, Liu Z, Liu X J, et al. DS-SLAM: A semantic visual SLAM towards dynamic environments[C]//2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2018: 1168-1174.
代码：github.com/ivipsourceco

76. Co-Fusion（实时分割与跟踪多物体）

论文：Rünz M, Agapito L. Co-fusion: Real-time segmentation, tracking and fusion of multiple objects[C]//2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2017: 4471-4478.
代码：github.com/martinruenz/ ；Video

77. DynamicFusion

论文：Newcombe R A, Fox D, Seitz S M. Dynamicfusion: Reconstruction and tracking of non-rigid scenes in real-time[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2015: 343-352.
代码：github.com/mihaibujanca

78. ReFusion（动态场景利用残差三维重建）

论文：Palazzolo E, Behley J, Lottes P, et al. ReFusion: 3D Reconstruction in Dynamic Environments for RGB-D Cameras Exploiting Residuals[J]. arXiv preprint arXiv:1905.02082, 2019.
代码：github.com/PRBonn/refus ；Video

79. DynSLAM（室外大规模稠密重建）

论文：Bârsan I A, Liu P, Pollefeys M, et al. Robust dense mapping for large-scale dynamic environments[C]//2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2018: 7510-7517.
代码：github.com/AndreiBarsan
作者博士学位论文：Barsan I A. Simultaneous localization and mapping in dynamic scenes[D]. ETH Zurich, Department of Computer Science, 2017.

80. VDO-SLAM（动态物体感知的 SLAM）

论文：Zhang J, Henein M, Mahony R, et al. VDO-SLAM: A Visual Dynamic Object-aware SLAM System[J]. arXiv preprint arXiv:2005.11052, 2020.（IJRR Under Review）

相关论文

IROS 2020 Robust Ego and Object 6-DoF Motion Estimation and Tracking
ICRA 2020 Dynamic SLAM: The Need For Speed

代码：github.com/halajun/VDO_ | video

六、Mapping（21 项）

针对建图的工作一方面是利用几何信息进行稠密重建，另一方面很多工作利用语义信息达到了很好的语义重建效果，三维重建、SFM 本身就是个很大的话题，开源代码也很多，以下方案收集地可能也不太全。

81. InfiniTAM（跨平台 CPU 实时重建）

论文：Prisacariu V A, Kähler O, Golodetz S, et al. Infinitam v3: A framework for large-scale 3d reconstruction with loop closure[J]. arXiv preprint arXiv:1708.00783, 2017.
代码：github.com/victorprad/I ；project page

82. BundleFusion

论文：Dai A, Nießner M, Zollhöfer M, et al. Bundlefusion: Real-time globally consistent 3d reconstruction using on-the-fly surface reintegration[J]. ACM Transactions on Graphics (TOG), 2017, 36(4): 76a.
代码：github.com/niessner/Bun ；工程地址

83. KinectFusion

论文：Newcombe R A, Izadi S, Hilliges O, et al. KinectFusion: Real-time dense surface mapping and tracking[C]//2011 10th IEEE International Symposium on Mixed and Augmented Reality. IEEE, 2011: 127-136.
代码：github.com/chrdiller/Ki

84. ElasticFusion

论文：Whelan T, Salas-Moreno R F, Glocker B, et al. ElasticFusion: Real-time dense SLAM and light source estimation[J]. The International Journal of Robotics Research, 2016, 35(14): 1697-1716.
代码：github.com/mp3guy/Elast

85. Kintinuous

ElasticFusion 同一个团队的工作，帝国理工 Stefan Leutenegger 谷歌学术
论文：Whelan T, Kaess M, Johannsson H, et al. Real-time large-scale dense RGB-D SLAM with volumetric fusion[J]. The International Journal of Robotics Research, 2015, 34(4-5): 598-626.
代码：github.com/mp3guy/Kinti

86. ElasticReconstruction

论文：Choi S, Zhou Q Y, Koltun V. Robust reconstruction of indoor scenes[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015: 5556-5565.
代码：github.com/qianyizh/Ela ；作者主页

87. FlashFusion

论文：Han L, Fang L. FlashFusion: Real-time Globally Consistent Dense 3D Reconstruction using CPU Computing[C]. RSS, 2018.
代码（一直没放出来）：github.com/lhanaf/Flash ；Project Page

88. RTAB-Map（激光视觉稠密重建）

论文：Labbé M, Michaud F. RTAB‐Map as an open‐source lidar and visual simultaneous localization and mapping library for large‐scale and long‐term online operation[J]. Journal of Field Robotics, 2019, 36(2): 416-446.
代码：github.com/introlab/rta ；Video ；project page

89. RobustPCLReconstruction（户外稠密重建）

论文：Lan Z, Yew Z J, Lee G H. Robust Point Cloud Based Reconstruction of Large-Scale Outdoor Scenes[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019: 9690-9698.
代码：github.com/ziquan111/Ro ；Video

90. plane-opt-rgbd（室内平面重建）

论文：Wang C, Guo X. Efficient Plane-Based Optimization of Geometry and Texture for Indoor RGB-D Reconstruction[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 2019: 49-53.
代码：github.com/chaowang15/p

91. DenseSurfelMapping（稠密表面重建）

论文：Wang K, Gao F, Shen S. Real-time scalable dense surfel mapping[C]//2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019: 6919-6925.
代码：github.com/HKUST-Aerial

92. surfelmeshing（网格重建）

论文：Schöps T, Sattler T, Pollefeys M. Surfelmeshing: Online surfel-based mesh reconstruction[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019.
代码：github.com/puzzlepaint/

93. DPPTAM（单目稠密重建）

论文：Concha Belenguer A, Civera Sancho J. DPPTAM: Dense piecewise planar tracking and mapping from a monocular sequence[C]//Proc. IEEE/RSJ Int. Conf. Intell. Rob. Syst. 2015 (ART-2015-92153).
代码：github.com/alejocb/dppt
相关研究：基于超像素的单目 SLAM：Using Superpixels in Monocular SLAM ICRA 2014 ；谷歌学术

94. VI-MEAN（单目视惯稠密重建）

论文：Yang Z, Gao F, Shen S. Real-time monocular dense mapping on aerial robots using visual-inertial fusion[C]//2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2017: 4552-4559.
代码：github.com/dvorak0/VI-M ；Video

95. REMODE（单目概率稠密重建）

论文：Pizzoli M, Forster C, Scaramuzza D. REMODE: Probabilistic, monocular dense reconstruction in real time[C]//2014 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2014: 2609-2616.
原始开源代码：github.com/uzh-rpg/rpg_
与 ORB-SLAM2 结合版本：github.com/ayushgaud/ORgithub.com/ayushgaud/OR

96. DeepFactors（实时的概率单目稠密 SLAM）

帝国理工学院戴森机器人实验室
论文：Czarnowski J, Laidlow T, Clark R, et al. DeepFactors: Real-Time Probabilistic Dense Monocular SLAM[J]. arXiv preprint arXiv:2001.05049, 2020.
代码：github.com/jczarnowski/ （还未放出）
其他论文：Bloesch M, Czarnowski J, Clark R, et al. CodeSLAM—learning a compact, optimisable representation for dense visual SLAM[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 2560-2568.

97. probabilistic_mapping（单目概率稠密重建）

港科沈邵劼老师团队
论文：Ling Y, Wang K, Shen S. Probabilistic dense reconstruction from a moving camera[C]//2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2018: 6364-6371.
代码：github.com/ygling2008/p
另外一篇稠密重建文章的代码一直没放出来 Github ：Ling Y, Shen S. Real‐time dense mapping for online processing and navigation[J]. Journal of Field Robotics, 2019, 36(5): 1004-1036.

98. ORB-SLAM2 单目半稠密建图

论文：Mur-Artal R, Tardós J D. Probabilistic Semi-Dense Mapping from Highly Accurate Feature-Based Monocular SLAM[C]//Robotics: Science and Systems. 2015, 2015.
代码（本身没有开源，贺博复现的一个版本）：github.com/HeYijia/ORB_
加上线段之后的半稠密建图

论文：He S, Qin X, Zhang Z, et al. Incremental 3d line segment extraction from semi-dense slam[C]//2018 24th International Conference on Pattern Recognition (ICPR). IEEE, 2018: 1658-1663.
代码：github.com/shidahe/semi
作者在此基础上用于指导远程抓取操作的一项工作：github.com/atlas-jj/ORB

99. Voxgraph（SDF 体素建图）

论文：Reijgwart V, Millane A, Oleynikova H, et al. Voxgraph: Globally Consistent, Volumetric Mapping Using Signed Distance Function Submaps[J]. IEEE Robotics and Automation Letters, 2019, 5(1): 227-234.
代码：github.com/ethz-asl/vox

100. SegMap（三维分割建图）

论文：Dubé R, Cramariuc A, Dugas D, et al. SegMap: 3d segment mapping using data-driven descriptors[J]. arXiv preprint arXiv:1804.09557, 2018.
代码：github.com/ethz-asl/seg

101. OpenREALM：无人机实时建图框架

论文：Kern A, Bobbe M, Khedar Y, et al. OpenREALM: Real-time Mapping for Unmanned Aerial Vehicles[J]. arXiv preprint arXiv:2009.10492, 2020.
代码：github.com/laxnpander/O

七、Optimization（6 项）

个人感觉优化可能是 SLAM 中最难的一部分了吧 +_+ ，我们一般都是直接用现成的因子图、图优化方案，要创新可不容易，分享三川小哥的入坑指难。

102. 后端优化库

GTSAM：github.com/borglab/gtsa ；官网
g2o：github.com/RainerKuemme
ceres：ceres-solver.org/

103. ICE-BA

论文：Liu H, Chen M, Zhang G, et al. Ice-ba: Incremental, consistent and efficient bundle adjustment for visual-inertial slam[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 1974-1982.
代码：github.com/baidu/ICE-BA

104. minisam（因子图最小二乘优化框架）

论文：Dong J, Lv Z. miniSAM: A Flexible Factor Graph Non-linear Least Squares Optimization Framework[J]. arXiv preprint arXiv:1909.00903, 2019.
代码：github.com/dongjing3309 ；文档

105. SA-SHAGO（几何基元图优化）

论文：Aloise I, Della Corte B, Nardi F, et al. Systematic Handling of Heterogeneous Geometric Primitives in Graph-SLAM Optimization[J]. IEEE Robotics and Automation Letters, 2019, 4(3): 2738-2745.
代码：srrg.gitlab.io/sashago-

106. MH-iSAM2（SLAM 优化器）

论文：Hsiao M, Kaess M. MH-iSAM2: Multi-hypothesis iSAM using Bayes Tree and Hypo-tree[C]//2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019: 1274-1280.
代码：bitbucket.org/rpl_cmu/m

107. MOLA（用于定位和建图的模块化优化框架）

论文：Blanco-Claraco J L. A Modular Optimization Framework for Localization and Mapping[J]. Proc. of Robotics: Science and Systems (RSS), FreiburgimBreisgau, Germany, 2019, 2.
代码：github.com/MOLAorg/mola ；Video ；使用文档

哇，你居然看完了，本文太长了，所以同时保存了一份 PDF 版供大家下载（13页）。

首发于：2020 年 03 月 31 日
Email：wuyanminmax@gmail.com
更新日志：
2020.03.31 补充 +1：TUM Basalt ，分类在 Sensor Fusion 中；
2020.04.04 补充 +1：Limo，分类在 Sensor Fusion 中；
2020.04.04 补充 +1：DynSLAM，分类在 Dynamic SLAM 中；
2020.04.25 补充 +2：Map2DFusion、CCM-SLAM，分类在 Geometric SLAM 中；
2020.04.25 补充 +2：北航 LARVIO、浙大 vig-init，分类在 Sensor Fusion 中；
2020.04.25 补充 +2：GCNv2_SLAM、semantic_suma 分类在 Sematic/Deep SLAM；
2020.05.24 补充 +2：vilib、Kimera-VIO 分类在 Sensor Fusion；
2020.05.24 补充 +1：Voxgraph 分类在 Mapping；
2020.05.24 补充 +1：Neural-SLAM 分类在 Sematic/Deep SLAM；
2020.05.24 补充 +1：VPS-SLAM 分类在 Multi-Landmarks/Object SLAM；
2020.06.27 补充 +1：maplab 分了在 Sensor Fusion 中；
2020.06.27 补充 +1：SegMap 分了在 Mapping 中；
2020.07.27 补充 +1：ORB-SLAM3 分类在 Geometric SLAM 中（本文的第 100 项开源代码的位置献给 ORB-SLAM3 ）；
2020.08.27 补充 +1：Structure-SLAM-PointLine 分类在 Multi-Landmarks/Object SLAM 中；
2020.08.27 补充 +1：VDO-SLAM 分类在 Dynamic SLAM 中；
2021.02.14 补充 +1：OpenREALM 分类在 Mapping 中；
2021.02.14 补充 +1：PL-VINS 分类在 Multi-Landmarks/Object SLAM 中；
2021.02.14 补充 +2：lili-om、CamVox 分类在 Sensor Fusion 中；
2021.02.14 补充 +1：TartanVO 分类在 Semantic / Deep SLAM 中。

来源：公众号羽林君，作者：小吴同学@知乎

仅用于学术分享，版权属于原作者。

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100多 项开源视觉 SLAM 方案够你用了吗？

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