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欢迎留言或者私信。

1 开源方案

1.1 Geometric SLAM （26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：https://github.com/Oxford-PTAM/PTAM-GPL
• 工程地址：http://www.robots.ox.ac.uk/~gk/PTAM/
• 作者其他研究：http://www.robots.ox.ac.uk/~gk/publications.html

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：https://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：https://github.com/hanmekim/SceneLib2

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：https://github.com/raulmur/ORB_SLAM2
• 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：https://vision.in.tum.de/research/vslam/dso

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：https://github.com/JakobEngel/dso
• 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：https://github.com/tum-vision/LDSO

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：https://github.com/tum-vision/lsd_slam

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：https://github.com/tum-vision/dvo_slam
• 代码 2：https://github.com/tum-vision/dvo
• 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：https://github.com/uzh-rpg/rpg_svo
• 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：https://github.com/jzubizarreta/dsm ；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：https://github.com/xdspacelab/openvslam ；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：https://github.com/izhengfan/se2lam
• 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：https://github.com/izhengfan/se2clam

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：https://github.com/AIBluefisher/GraphSfM

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：https://github.com/sunghoon031/LCSD_SLAM ；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：https://github.com/fabianschenk/RESLAM ； 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：https://github.com/jiawei-mo/scale_optimization

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：https://github.com/ETH3D/badslam

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：https://github.com/zdzhaoyong/GSLAM

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：https://github.com/zanazakaryaie/ARM-VO

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：https://github.com/MaaniGhaffari/cvo-rgbd

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：https://github.com/zdzhaoyong/Map2DFusion

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：https://github.com/VIS4ROB-lab/ccm_slam   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：https://github.com/UZ-SLAMLab/ORB_SLAM3 | Video

24. OV²SLAM（完全实时在线多功能 SLAM）

• 论文：Ferrera M, Eudes A, Moras J, et al. OV $^{2} $ SLAM: A Fully Online and Versatile Visual SLAM for Real-Time Applications[J]. IEEE Robotics and Automation Letters, 2021, 6(2): 1399-1406.
• 代码：https://github.com/ov2slam/ov2slam

25. ESVO（基于事件的双目视觉里程计）

• 论文：Zhou Y, Gallego G, Shen S. Event-based stereo visual odometry[J]. IEEE Transactions on Robotics, 2021.
• 代码：https://github.com/HKUST-Aerial-Robotics/ESVO

26. VOLDOR-SLAM（实时稠密非直接法 SLAM）

• 论文：Min Z, Dunn E. VOLDOR-SLAM: For the Times When Feature-Based or Direct Methods Are Not Good Enough[J]. arXiv preprint arXiv:2104.06800, 2021.
• 代码：https://github.com/htkseason/VOLDOR

1.2 Semantic / Deep SLAM （17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）。

1. 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：https://github.com/martinruenz/maskfusion

2. 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：https://github.com/seaun163/semanticfusion

3. 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：https://github.com/shichaoy/semantic_3d_mapping

4. 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：https://github.com/MIT-SPARK/Kimera ；演示视频

5. 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：https://github.com/cognav/NeuroSLAM
• 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

6. 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）：https://github.com/montrealrobotics/gradSLAM ；项目主页，演示视频

7. ORB-SLAM2 + 目标检测/分割的方案语义建图

• https://github.com/floatlazer/semantic_slam
• https://github.com/qixuxiang/orb-slam2_with_semantic_labelling
• https://github.com/Ewenwan/ORB_SLAM2_SSD_Semantic

8. 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：https://github.com/navganti/SIVO

9. 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 (IROS 2019)
• 代码：https://github.com/AnshanTJU/FILD

10. 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：https://github.com/CIFASIS/object-detection-sptam

11. 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：https://github.com/jmtc7/mapSlammer

12. 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：https://github.com/bogus2000/NOLBO

13. 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：https://github.com/jiexiong2016/GCNv2_SLAM   Video

14. 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：https://github.com/PRBonn/semantic_suma/ ；Video

15. 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：https://github.com/devendrachaplot/Neural-SLAM

16. 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：https://github.com/castacks/tartanvo
• 数据集：IROS2020 TartanAir: A Dataset to Push the Limits of Visual SLAM，数据集地址

17. DF-VO

• 论文：Zhan H, Weerasekera C S, Bian J W, et al. DF-VO: What Should Be Learnt for Visual Odometry?[J]. arXiv preprint arXiv:2103.00933, 2021.
  • Zhan H, Weerasekera C S, Bian J W, et al. Visual odometry revisited: What should be learnt?[C]//2020 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2020: 4203-4210.
• 代码：https://github.com/Huangying-Zhan/DF-VO

1.3 Multi-Landmarks / Object SLAM （15项）

多路标的点、线、平面 SLAM 和物体级 SLAM

1. 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：https://github.com/rubengooj/pl-svo

2. 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：https://github.com/rubengooj/stvo-pl

3. 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：https://github.com/rubengooj/pl-slam
• 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.

4. 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：https://github.com/HeYijia/PL-VIO
• VINS + 线段：https://github.com/Jichao-Peng/VINS-Mono-Optimization

5. 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：https://github.com/alexandervakhitov/lld-slam ；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.

6. 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：https://github.com/LRMPUT/PlaneSLAM
• 作者另外一项开源代码，没有找到对应的论文：https://github.com/LRMPUT/PUTSLAM

7. 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：https://gitlab.com/gferrer/eigen-factors-iros2019 ；演示视频

8. 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：https://github.com/LRMPUT/PlaneLoc

9. 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：https://github.com/shichaoy/pop_up_slam

10. 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：https://github.com/BeipengMu/objectSLAM ；Video

11. 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：https://github.com/ethz-asl/voxblox-plusplus

12. 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：https://github.com/shichaoy/cube_slam
  +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.

13. 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：https://bitbucket.org/hridaybavle/semantic_slam/src/master/

14. Structure-SLAM （低纹理环境下点线 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：https://github.com/yanyan-li/Structure-SLAM-PointLine

15. 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：https://github.com/cnqiangfu/PL-VINS

1.4 Sensor Fusion （23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。

1. 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：https://github.com/KumarRobotics/msckf_vio ；Video

2. 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：https://github.com/ethz-asl/rovio ；Video

3. 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：https://github.com/rpng/R-VIO ；Video
• VI_ORB_SLAM2：https://github.com/YoujieXia/VI_ORB_SLAM2

4. 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：https://github.com/ethz-asl/okvis

5. 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：https://github.com/jingpang/LearnVIORB （VIORB 本身是没有开源的，这是王京大佬复现的一个版本）

6. 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：https://github.com/HKUST-Aerial-Robotics/VINS-Mono
• 双目版 VINS-Fusion：https://github.com/HKUST-Aerial-Robotics/VINS-Fusion
• 移动段 VINS-mobile：https://github.com/HKUST-Aerial-Robotics/VINS-Mobile

7. 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：https://github.com/STAR-Center/VINS-RGBD ；Video

8. 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：https://github.com/rpng/open_vins

9. 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：https://github.com/ethz-asl/versavis

10. 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：https://github.com/rpng/cpi ；Video

11. 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：https://github.com/VladyslavUsenko/basalt-mirror ；Video；Project Page

12. 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：https://github.com/johannes-graeter/limo ； Video

13. 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：https://github.com/PetWorm/LARVIO
• 北航邱笑晨博士的一项工作

14. 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：https://github.com/zju3dv/vig-init
• 浙大章国峰老师组的一项工作

15. 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：https://github.com/uzh-rpg/vilib

16. 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：https://github.com/MIT-SPARK/Kimera-VIO

17. 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：https://github.com/ethz-asl/maplab
• 多会话建图，地图合并，视觉惯性批处理优化和闭环

18. 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：https://github.com/KIT-ISAS/lili-om

19. 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：https://github.com/ISEE-Technology/CamVox

20. SSL_SLAM：固态 LiDAR 轻量级 3D 定位与建图

• 论文：Wang H, Wang C, Xie L. Lightweight 3-D Localization and Mapping for Solid-State LiDAR[J]. IEEE Robotics and Automation Letters, 2021, 6(2): 1801-1807.
• 代码：https://github.com/wh200720041/SSL_SLAM

21. r2live：LiDAR-Inertial-Visual 紧耦合

• 论文：Lin J, Zheng C, Xu W, et al. R2LIVE: A Robust, Real-time, LiDAR-Inertial-Visual tightly-coupled state Estimator and mapping[J]. arXiv preprint arXiv:2102.12400, 2021.
• 代码：https://github.com/hku-mars/r2live

22. GVINS：GNSS-视觉-惯导紧耦合

• 论文：Cao S, Lu X, Shen S. GVINS: Tightly Coupled GNSS-Visual-Inertial for Smooth and Consistent State Estimation[J]. arXiv e-prints, 2021: arXiv: 2103.07899.
• 代码：https://github.com/HKUST-Aerial-Robotics/GVINS

23. LVI-SAM：Lidar-Visual-Inertial 建图与定位

• 论文：Shan T, Englot B, Ratti C, et al. LVI-SAM: Tightly-coupled Lidar-Visual-Inertial Odometry via Smoothing and Mapping[J]. arXiv preprint arXiv:2104.10831, 2021. (ICRA2021)
• 代码：https://github.com/TixiaoShan/LVI-SAM

1.5 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

1. 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：https://github.com/ganlumomo/DynamicSemanticMapping ；wiki

2. 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：https://github.com/ivipsourcecode/DS-SLAM

3. 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：https://github.com/martinruenz/co-fusion ； Video

4. 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：https://github.com/mihaibujanca/dynamicfusion

5. 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：https://github.com/PRBonn/refusion ；Video

6. 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：https://github.com/AndreiBarsan/DynSLAM
• 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.

7. 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：https://github.com/halajun/VDO_SLAM | video

1.6 Mapping （22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。

1. 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：https://github.com/victorprad/InfiniTAM ；project page

2. 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：https://github.com/niessner/BundleFusion ；工程地址

3. 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：https://github.com/chrdiller/KinectFusionApp

4. 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：https://github.com/mp3guy/ElasticFusion

5. 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：https://github.com/mp3guy/Kintinuous

6. 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：https://github.com/qianyizh/ElasticReconstruction ；作者主页

7. 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）：https://github.com/lhanaf/FlashFusion ； Project Page

8. 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：https://github.com/introlab/rtabmap ；Video ；project page

9. 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：https://github.com/ziquan111/RobustPCLReconstruction ；Video

10. 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：https://github.com/chaowang15/plane-opt-rgbd

11. 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：https://github.com/HKUST-Aerial-Robotics/DenseSurfelMapping

12. 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：https://github.com/puzzlepaint/surfelmeshing

13. 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：https://github.com/alejocb/dpptam
• 相关研究：基于超像素的单目 SLAM：Using Superpixels in Monocular SLAM ICRA 2014 ；谷歌学术

14. 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：https://github.com/dvorak0/VI-MEAN ；Video

15. 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：https://github.com/uzh-rpg/rpg_open_remode
• 与 ORB-SLAM2 结合版本：https://github.com/ayushgaud/ORB_SLAM2 https://github.com/ayushgaud/ORB_SLAM2

16. 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：https://github.com/jczarnowski/DeepFactors （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.

17. 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：https://github.com/ygling2008/probabilistic_mapping
• 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.

18. 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）：https://github.com/HeYijia/ORB_SLAM2
• 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：https://github.com/shidahe/semidense-lines
  • 作者在此基础上用于指导远程抓取操作的一项工作：https://github.com/atlas-jj/ORB-SLAM-free-space-carving

19. 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：https://github.com/ethz-asl/voxgraph

20. 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：https://github.com/ethz-asl/segmap

21. 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：https://github.com/laxnpander/OpenREALM

22. c-blox：可拓展的 TSDF 稠密建图

• 论文：Millane A, Taylor Z, Oleynikova H, et al. C-blox: A scalable and consistent tsdf-based dense mapping approach[C]//2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2018: 995-1002.
• 代码：https://github.com/ethz-asl/cblox

1.7 Optimization （6项）

优化可能是 SLAM 中最难的一部分了吧 +_+ ，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

1. 后端优化库

• GTSAM：https://github.com/borglab/gtsam ；官网
• g2o：https://github.com/RainerKuemmerle/g2o
• ceres：http://ceres-solver.org/

2. 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：https://github.com/baidu/ICE-BA

3. 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：https://github.com/dongjing3309/minisam ； 文档

4. 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：https://srrg.gitlab.io/sashago-website/index.html#

5. 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：https://bitbucket.org/rpl_cmu/mh-isam2_lib/src/master/

6. 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：https://github.com/MOLAorg/mola ；Video ；使用文档

---

2. 优秀作者与实验室

这一部分整理之后发布在知乎（2020 年 4 月 19 日）：https://zhuanlan.zhihu.com/p/130530891

1. 美国卡耐基梅陇大学机器人研究所

• 研究方向：机器人感知、结构，服务型、运输、制造业、现场机器
• 研究所主页：https://www.ri.cmu.edu/
• 下属 Field Robotic Center 主页：https://frc.ri.cmu.edu/
• 发表论文：https://www.ri.cmu.edu/pubs/
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