（汇总篇）语义SLAM相关开源方案| 全球优秀作者与实验室

这篇具有很好参考价值的文章主要介绍了（汇总篇）语义SLAM相关开源方案| 全球优秀作者与实验室 | SLAM学习资料整理。希望对大家有所帮助。如果存在错误或未考虑完全的地方，请大家不吝赐教，您也可以点击"举报违法"按钮提交疑问。

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/
👦 Michael Kaess：个人主页，谷歌学术
👦 Sebastian Scherer：个人主页，谷歌学术
📜 Kaess M, Ranganathan A, Dellaert F. iSAM: Incremental smoothing and mapping[J]. IEEE Transactions on Robotics, 2008, 24(6): 1365-1378.
📜 Hsiao M, Westman E, Zhang G, et al. Keyframe-based dense planar SLAM[C]//2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2017: 5110-5117.
📜 Kaess M. Simultaneous localization and mapping with infinite planes[C]//2015 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2015: 4605-4611.

2. 美国加州大学圣地亚哥分校语境机器人研究所

研究方向：多模态环境理解，语义导航，自主信息获取
实验室主页：https://existentialrobotics.org/index.html
发表论文汇总：https://existentialrobotics.org/pages/publications.html
👦 Nikolay Atanasov：个人主页谷歌学术
- 机器人状态估计与感知课程 ppt：https://natanaso.github.io/ece276a2019/schedule.html
📜 语义 SLAM 经典论文：Bowman S L, Atanasov N, Daniilidis K, et al. Probabilistic data association for semantic slam[C]//2017 IEEE international conference on robotics and automation (ICRA). IEEE, 2017: 1722-1729.
📜 实例网格模型定位与建图：Feng Q, Meng Y, Shan M, et al. Localization and Mapping using Instance-specific Mesh Models[C]//2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2019: 4985-4991.
📜 基于事件相机的 VIO：Zihao Zhu A, Atanasov N, Daniilidis K. Event-based visual inertial odometry[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017: 5391-5399.

3. 美国特拉华大学机器人感知与导航组

研究方向：SLAM、VINS、语义定位与建图等
实验室主页：https://sites.udel.edu/robot/
发表论文汇总：https://sites.udel.edu/robot/publications/
Github 地址：https://github.com/rpng?page=2
📜 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 ）
📜 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 ）
📜 Zuo X, Geneva P, Yang Y, et al. Visual-Inertial Localization With Prior LiDAR Map Constraints[J]. IEEE Robotics and Automation Letters, 2019, 4(4): 3394-3401.
📜 Zuo X, Ye W, Yang Y, et al. Multimodal localization: Stereo over LiDAR map[J]. Journal of Field Robotics, 2020 （左星星博士谷歌学术）
👦 黄国权教授主页

4. 美国麻省理工学院航空航天实验室

研究方向：位姿估计与导航，路径规划，控制与决策，机器学习与强化学习
实验室主页：http://acl.mit.edu/
发表论文：http://acl.mit.edu/publications （实验室的学位论文也可以在这里找到）
👦 Jonathan P. How 教授：个人主页谷歌学术
👦 Kasra Khosoussi（SLAM 图优化）：谷歌学术
📜 物体级 SLAM：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）
📜 物体级 SLAM 导航：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.
📜 SLAM 的图优化：Khosoussi, K., Giamou, M., Sukhatme, G., Huang, S., Dissanayake, G., and How, J. P., Reliable Graphs for SLAM [C]//International Journal of Robotics Research (IJRR), 2019.

5. 美国麻省理工学院 SPARK 实验室

研究方向：移动机器人环境感知
实验室主页：http://web.mit.edu/sparklab/
👦 Luca Carlone 教授：个人主页谷歌学术
📜 SLAM 经典综述：Cadena C, Carlone L, Carrillo H, et al. Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age[J]. IEEE Transactions on robotics, 2016, 32(6): 1309-1332.
📜 VIO 流形预积分：Forster C, Carlone L, Dellaert F, et al. On-Manifold Preintegration for Real-Time Visual–Inertial Odometry[J]. IEEE Transactions on Robotics, 2016, 33(1): 1-21.
📜 开源语义 SLAM：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 ）

6. 美国麻省理工学院海洋机器人组

研究方向：水下或陆地移动机器人导航与建图
实验室主页：https://marinerobotics.mit.edu/ （隶属于 MIT 计算机科学与人工智能实验室）
👦 John Leonard 教授：谷歌学术
发表论文汇总：https://marinerobotics.mit.edu/biblio
📜 面向物体的 SLAM：Finman R, Paull L, Leonard J J. Toward object-based place recognition in dense rgb-d maps[C]//ICRA Workshop Visual Place Recognition in Changing Environments, Seattle, WA. 2015.
📜 拓展 KinectFusion：Whelan T, Kaess M, Fallon M, et al. Kintinuous: Spatially extended kinectfusion[J]. 2012.
📜 语义 SLAM 概率数据关联：Doherty K, Fourie D, Leonard J. Multimodal semantic slam with probabilistic data association[C]//2019 international conference on robotics and automation (ICRA). IEEE, 2019: 2419-2425.

7. 美国明尼苏达大学多元自主机器人系统实验室

研究方向：视觉、激光、惯性导航系统，移动设备大规模三维建模与定位
实验室主页：http://mars.cs.umn.edu/index.php
发表论文汇总：http://mars.cs.umn.edu/publications.php
👦 Stergios I. Roumeliotis：个人主页，谷歌学术
📜 移动设备 VIO：Wu K, Ahmed A, Georgiou G A, et al. A Square Root Inverse Filter for Efficient Vision-aided Inertial Navigation on Mobile Devices[C]//Robotics: Science and Systems. 2015, 2.（项目主页：http://mars.cs.umn.edu/research/sriswf.php ）
📜 移动设备大规模三维半稠密建图：Guo C X, Sartipi K, DuToit R C, et al. Resource-aware large-scale cooperative three-dimensional mapping using multiple mobile devices[J]. IEEE Transactions on Robotics, 2018, 34(5): 1349-1369. （项目主页：http://mars.cs.umn.edu/research/semi_dense_mapping.php ）
📜 VIO 相关研究：http://mars.cs.umn.edu/research/vins_overview.php

8. 美国宾夕法尼亚大学 Vijay Kumar 实验室

研究方向：自主微型无人机
实验室主页：https://www.kumarrobotics.org/
发表论文：https://www.kumarrobotics.org/publications/
研究成果视频：https://www.youtube.com/user/KumarLabPenn/videos
📜 无人机半稠密 VIO：Liu W, Loianno G, Mohta K, et al. Semi-Dense Visual-Inertial Odometry and Mapping for Quadrotors with SWAP Constraints[C]//2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2018: 1-6.
📜 语义数据关联：Liu X, Chen S W, Liu C, et al. Monocular Camera Based Fruit Counting and Mapping with Semantic Data Association[J]. IEEE Robotics and Automation Letters, 2019, 4(3): 2296-2303.

9. Srikumar Ramalingam（美国犹他大学计算机学院）

研究方向：三维重构、语义分割、视觉 SLAM、图像定位、深度神经网络
👦 Srikumar Ramalingam：个人主页谷歌学术
📜 点面 SLAM：Taguchi Y, Jian Y D, Ramalingam S, et al. Point-plane SLAM for hand-held 3D sensors[C]//2013 IEEE international conference on robotics and automation. IEEE, 2013: 5182-5189.
📜 点线定位：Ramalingam S, Bouaziz S, Sturm P. Pose estimation using both points and lines for geo-localization[C]//2011 IEEE International Conference on Robotics and Automation. IEEE, 2011: 4716-4723.（视频）
📜 2D 3D 定位：Ataer-Cansizoglu E, Taguchi Y, Ramalingam S. Pinpoint SLAM: A hybrid of 2D and 3D simultaneous localization and mapping for RGB-D sensors[C]//2016 IEEE international conference on robotics and automation (ICRA). IEEE, 2016: 1300-1307.（视频）

10. Frank Dellaert（美国佐治亚理工学院机器人与智能机器研究中心）

研究方向：SLAM，图像时空重构
👦 个人主页，谷歌学术
📜 因子图：Dellaert F. Factor graphs and GTSAM: A hands-on introduction[R]. Georgia Institute of Technology, 2012. （GTSAM 代码：http://borg.cc.gatech.edu/ ）
📜 多机器人分布式 SLAM：Cunningham A, Wurm K M, Burgard W, et al. Fully distributed scalable smoothing and mapping with robust multi-robot data association[C]//2012 IEEE International Conference on Robotics and Automation. IEEE, 2012: 1093-1100.
📜 Choudhary S, Trevor A J B, Christensen H I, et al. SLAM with object discovery, modeling and mapping[C]//2014 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 2014: 1018-1025.

11. Patricio Vela （美国佐治亚理工学院智能视觉与自动化实验室）

研究方向：机器人控制、定位与导航
实验室主页：http://ivalab.gatech.edu/
👦 Patricio Vela 个人主页
👦 赵轶璞个人主页谷歌学术
📜 Zhao Y, Smith J S, Karumanchi S H, et al. Closed-Loop Benchmarking of Stereo Visual-Inertial SLAM Systems: Understanding the Impact of Drift and Latency on Tracking Accuracy[J]. arXiv preprint arXiv:2003.01317, 2020.
📜 Zhao Y, Vela P A. Good feature selection for least squares pose optimization in VO/VSLAM[C]//2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2018: 1183-1189.（代码：https://github.com/ivalab/FullResults_GoodFeature ）
📜 Zhao Y, Vela P A. Good line cutting: Towards accurate pose tracking of line-assisted VO/VSLAM[C]//Proceedings of the European Conference on Computer Vision (ECCV). 2018: 516-531. （代码：https://github.com/ivalab/GF_PL_SLAM ）

12. 加拿大蒙特利尔大学机器人与嵌入式 AI 实验室

研究方向：SLAM，不确定性建模
实验室主页：http://montrealrobotics.ca/
👦 Liam Paull 教授：个人主页谷歌学术
📜 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）
📜 Murthy Jatavallabhula K, Iyer G, Paull L. gradSLAM: Dense SLAM meets Automatic Differentiation[J]. arXiv preprint arXiv:1910.10672, 2019.（代码：https://github.com/montrealrobotics/gradSLAM ）

13. 加拿大舍布鲁克大学智能、交互、综合、跨学科机器人实验室

研究方向：移动机器人软硬件设计
实验室主页：https://introlab.3it.usherbrooke.ca/
📜 激光视觉稠密重建：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
- 项目主页：http://introlab.github.io/rtabmap/

14. 瑞士苏黎世大学机器人与感知课题组

研究方向：移动机器人、无人机环境感知与导航，VISLAM，事件相机
实验室主页：http://rpg.ifi.uzh.ch/index.html
发表论文汇总：http://rpg.ifi.uzh.ch/publications.html
Github 代码公开地址：https://github.com/uzh-rpg
📜 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.
📜 VO/VIO 轨迹评估工具 rpg_trajectory_evaluation：https://github.com/uzh-rpg/rpg_trajectory_evaluation
📜 事件相机项目主页：http://rpg.ifi.uzh.ch/research_dvs.html
👦 人物：Davide Scaramuzza 张子潮

15. 瑞士苏黎世联邦理工计算机视觉与几何实验室

研究方向：定位、三维重建、语义分割、机器人视觉
实验室主页：http://www.cvg.ethz.ch/index.php
发表论文：http://www.cvg.ethz.ch/publications/
📜 视觉语义里程计：Lianos K N, Schonberger J L, Pollefeys M, et al. Vso: Visual semantic odometry[C]//Proceedings of the European conference on computer vision (ECCV). 2018: 234-250.
📜 视觉语义定位：CVPR 2018 Semantic visual localization
- 作者博士学位论文：2018 Robust Methods for Accurate and Efficient 3D Modeling from Unstructured Imagery
📜 大规模户外建图：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.
👦 Marc Pollefeys：个人主页，谷歌学术
👦 Johannes L. Schönberger：个人主页，谷歌学术

16. 英国帝国理工学院戴森机器人实验室

研究方向：机器人视觉场景与物体理解、机器人操纵
实验室主页：https://www.imperial.ac.uk/dyson-robotics-lab/
发表论文：https://www.imperial.ac.uk/dyson-robotics-lab/publications/
代表性工作：MonoSLAM、CodeSLAM、ElasticFusion、KinectFusion
- 📜 ElasticFusion：Whelan T, Leutenegger S, Salas-Moreno R, et al. ElasticFusion: Dense SLAM without a pose graph[C]. Robotics: Science and Systems, 2015.（代码：https://github.com/mp3guy/ElasticFusion ）
- 📜 Semanticfusion：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 ）
- 📜 Code-SLAM：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.
👦 Andrew Davison：谷歌学术

17. 英国牛津大学信息工程学

研究方向：SLAM、目标跟踪、运动结构、场景增强、移动机器人运动规划、导航与建图等等等
实验室主页：http://www.robots.ox.ac.uk/
- 主动视觉实验室：http://www.robots.ox.ac.uk/ActiveVision/
- 牛津机器人学院：https://ori.ox.ac.uk/
发表论文汇总：
- 主动视觉实验室：http://www.robots.ox.ac.uk/ActiveVision/Publications/index.html
- 机器人学院：https://ori.ox.ac.uk/publications/papers/
代表性工作：
- 📜 Klein G, Murray D. PTAM: Parallel tracking and mapping for small AR workspaces[C]//2007 6th IEEE and ACM international symposium on mixed and augmented reality. IEEE, 2007: 225-234.
- 📜 RobotCar 数据集：https://robotcar-dataset.robots.ox.ac.uk/
👦 人物（谷歌学术）：David Murray Maurice Fallon
部分博士学位论文可以在这里搜到：https://ora.ox.ac.uk/

18. 德国慕尼黑工业大学计算机视觉组

研究方向：三维重建、机器人视觉、深度学习、视觉 SLAM 等
实验室主页：https://vision.in.tum.de/research/vslam
发表论文汇总：https://vision.in.tum.de/publications
代表作：DSO、LDSO、LSD_SLAM、DVO_SLAM
- 📜 DSO：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 ）
- 📜 LSD-SLAM： 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 ）2.
Github 地址：https://github.com/tum-vision
👦 Daniel Cremers 教授：个人主页谷歌学术
👦 Jakob Engel（LSD-SLAM，DSO 作者）：个人主页谷歌学术

19. 德国马克斯普朗克智能系统研究所嵌入式视觉组

研究方向：智能体自主环境理解、导航与物体操纵
实验室主页：https://ev.is.tuebingen.mpg.de/
👦 负责人 Jörg Stückler（前 TUM 教授）：个人主页谷歌学术
📜 发表论文汇总：https://ev.is.tuebingen.mpg.de/publications
Kasyanov A, Engelmann F, Stückler J, et al. Keyframe-based visual-inertial online SLAM with relocalization[C]//2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2017: 6662-6669.
📜 Strecke M, Stuckler J. EM-Fusion: Dynamic Object-Level SLAM with Probabilistic Data Association[C]//Proceedings of the IEEE International Conference on Computer Vision. 2019: 5865-5874.
📜 Usenko, V., Demmel, N., Schubert, D., Stückler, J., Cremers, D. Visual-Inertial Mapping with Non-Linear Factor Recovery IEEE Robotics and Automation Letters (RA-L), 5, 2020

20. 德国弗莱堡大学智能自主系统实验室

研究方向：多机器人导航与协作，环境建模与状态估计
实验室主页：http://ais.informatik.uni-freiburg.de/index_en.php
发表论文汇总：http://ais.informatik.uni-freiburg.de/publications/index_en.php （学位论文也可以在这里找到）
👦 Wolfram Burgard：谷歌学术
开放数据集：http://aisdatasets.informatik.uni-freiburg.de/
📜 RGB-D SLAM：Endres F, Hess J, Sturm J, et al. 3-D mapping with an RGB-D camera[J]. IEEE transactions on robotics, 2013, 30(1): 177-187.（代码：https://github.com/felixendres/rgbdslam_v2 ）
📜 跨季节的 SLAM：Naseer T, Ruhnke M, Stachniss C, et al. Robust visual SLAM across seasons[C]//2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2015: 2529-2535.
📜 博士学位论文：Robust Graph-Based Localization and Mapping 2015
📜 博士学位论文：Discovering and Leveraging Deep Multimodal Structure for Reliable Robot Perception and Localization 2019
📜 博士学位论文：Robot Localization and Mapping in Dynamic Environments 2019

21. 西班牙萨拉戈萨大学机器人、感知与实时组 SLAM 实验室

研究方向：视觉 SLAM、物体 SLAM、非刚性 SLAM、机器人、增强现实
实验室主页：http://robots.unizar.es/slamlab/
发表论文：http://robots.unizar.es/slamlab/?extra=3 （论文好像没更新，可以访问下面实验室大佬的谷歌学术查看最新论文）
👦 J. M. M. Montiel：谷歌学术
📜 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.
Gálvez-López D, Salas M, Tardós J D, et al. Real-time monocular object slam[J]. Robotics and Autonomous Systems, 2016, 75: 435-449.
📜 Strasdat H, Montiel J M M, Davison A J. Real-time monocular SLAM: Why filter?[C]//2010 IEEE International Conference on Robotics and Automation. IEEE, 2010: 2657-2664.
📜 Zubizarreta J, Aguinaga I, Montiel J M M. Direct sparse mapping[J]. arXiv preprint arXiv:1904.06577, 2019.
- 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.

22. 西班牙马拉加大学机器感知与智能机器人课题组

研究方向：自主机器人、人工嗅觉、计算机视觉
实验室主页：http://mapir.uma.es/mapirwebsite/index.php/topics-2.html
发表论文汇总：http://mapir.isa.uma.es/mapirwebsite/index.php/publications-menu-home.html
📜 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.（代码：https://github.com/rubengooj/pl-slam ）
👦 Francisco-Angel Moreno
👦 Ruben Gomez-Ojeda 点线 SLAM
- 📜 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 ）
- 📜 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 ）
- 📜 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 ）

23. Alejo Concha（Oculus VR，西班牙萨拉戈萨大学）

研究方向：SLAM，单目稠密重建，传感器融合
👦 个人主页：https://sites.google.com/view/alejoconcha/ 谷歌学术
Github：https://github.com/alejocb
📜 IROS 2015 单目平面重建：DPPTAM: Dense piecewise planar tracking and mapping from a monocular sequence （代码：https://github.com/alejocb/dpptam ）
📜 IROS 2017 开源 RGB-D SLAM：RGBDTAM: A Cost-Effective and Accurate RGB-D Tracking and Mapping System（代码：https://github.com/alejocb/rgbdtam ）
📜 ICRA 2016：Visual-inertial direct SLAM
📜 ICRA 2014：Using Superpixels in Monocular SLAM
RSS 2014：Manhattan and Piecewise-Planar Constraints for Dense Monocular Mapping

24. 奥地利格拉茨技术大学计算机图形学与视觉研究所

研究方向：AR/VR，机器人视觉，机器学习，目标识别与三维重建
实验室主页：https://www.tugraz.at/institutes/icg/home/
👦 Friedrich Fraundorfer 教授：团队主页谷歌学术
- 📜 Visual Odometry: Part I The First 30 Years and Fundamentals
- 📜 Visual Odometry: Part II: Matching, Robustness, Optimization, and Applications
- 📜 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 ）
👦 Dieter Schmalstieg 教授：团队主页谷歌学术
- 📜 教科书：Augmented Reality: Principles and Practice
- 📜 Arth C, Pirchheim C, Ventura J, et al. Instant outdoor localization and slam initialization from 2.5 d maps[J]. IEEE transactions on visualization and computer graphics, 2015, 21(11): 1309-1318.
- 📜 Hachiuma R, Pirchheim C, Schmalstieg D, et al. DetectFusion: Detecting and Segmenting Both Known and Unknown Dynamic Objects in Real-time SLAM[J]. arXiv preprint arXiv:1907.09127, 2019.

25. 波兰波兹南工业大学移动机器人实验室

研究方向：SLAM，机器人运动规划，控制
实验室主页：http://lrm.put.poznan.pl/
Github 主页：https://github.com/LRMPUT
📜 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 ）
📜 Wietrzykowski J, Skrzypczyński P. PlaneLoc: Probabilistic global localization in 3-D using local planar features[J]. Robotics and Autonomous Systems, 2019.（代码：https://github.com/LRMPUT/PlaneLoc ）
📜 PUTSLAM：http://lrm.put.poznan.pl/putslam/

26. Alexander Vakhitov（三星莫斯科 AI 中心）

研究方向：SLAM，几何视觉
👦 个人主页：https://alexandervakhitov.github.io/ ，谷歌学术
📜 点线 SLAM：ICRA 2017 PL-SLAM: Real-time monocular visual SLAM with points and lines
📜 点线定位：Pumarola A, Vakhitov A, Agudo A, et al. Relative localization for aerial manipulation with PL-SLAM[M]//Aerial Robotic Manipulation. Springer, Cham, 2019: 239-248.
📜 学习型线段：IEEE Access 2019 Learnable line segment descriptor for visual SLAM（代码：https://github.com/alexandervakhitov/lld-slam ）

27. 澳大利亚昆士兰科技大学机器人技术中心

研究方向：脑启发式机器人，采矿机器人，机器人视觉
实验室主页：https://www.qut.edu.au/research/centre-for-robotics
开源代码：https://research.qut.edu.au/qcr/open-source-code/
👦 Niko Sünderhauf：个人主页，谷歌学术
- 📜 RA-L 2018 二次曲面 SLAM：QuadricSLAM: Dual quadrics from object detections as landmarks in object-oriented SLAM
- 📜 Nicholson L, Milford M, Sunderhauf N. QuadricSLAM: Dual quadrics as SLAM landmarks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 2018: 313-314.
- 📜 Semantic SLAM 项目主页：http://www.semanticslam.ai/
- 📜 IROS 2017：Meaningful maps with object-oriented semantic mapping
👦 Michael Milford：谷歌学术 https://scholar.google.com/citations?user=TDSmCKgAAAAJ&hl=zh-CN&oi=ao
- 📜 ICRA 2012：SeqSLAM: Visual route-based navigation for sunny summer days and stormy winter nights （代码：https://michaelmilford.com/seqslam/）
- 📜 Ball D, Heath S, Wiles J, et al. OpenRatSLAM: an open source brain-based SLAM system[J]. Autonomous Robots, 2013, 34(3): 149-176.（代码：https://openslam-org.github.io/openratslam.html ）
- 📜 Yu F, Shang J, Hu Y, et al. NeuroSLAM: a brain-inspired SLAM system for 3D environments[J]. Biological Cybernetics, 2019, 113(5-6): 515-545. （代码：https://github.com/cognav/NeuroSLAM ）

28. 澳大利亚机器人视觉中心

研究方向：机器人感知、理解与学习（集合了昆士兰科技大学，澳大利亚国立大学，阿德莱德大学，昆士兰大学等学校机器人领域的研究者）
实验室主页：https://www.roboticvision.org/
人物：https://www.roboticvision.org/rv_person_category/researchers/
发表论文汇总：https://www.roboticvision.org/publications/scientific-publications/
👦 Yasir Latif：个人主页，谷歌学术
- 📜 Latif Y, Cadena C, Neira J. Robust loop closing over time for pose graph SLAM[J]. The International Journal of Robotics Research, 2013, 32(14): 1611-1626.
- 📜 Latif Y, Cadena C, Neira J. Robust loop closing over time[C]//Proc. Robotics: Science Systems. 2013: 233-240.（代码：https://github.com/ylatif/rrr ）
👦 Ian D Reid：谷歌学术：https://scholar.google.com/citations?user=ATkNLcQAAAAJ&hl=zh-CN&oi=sra
- 📜 ICRA 2019：Real-time monocular object-model aware sparse SLAM
- 📜 Reid I. Towards semantic visual SLAM[C]//2014 13th International Conference on Control Automation Robotics & Vision (ICARCV). IEEE, 2014: 1-1.

29. 日本国立先进工业科学技术研究所

人工智能研究中心：https://www.airc.aist.go.jp/en/intro/
👦 Ken Sakurada：个人主页，谷歌学术
- 📜 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 ）
👦 Shuji Oishi：谷歌学术
- 📜 极稠密特征点建图：Yokozuka M, Oishi S, Thompson S, et al. VITAMIN-E: visual tracking and MappINg with extremely dense feature points[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2019: 9641-9650.
- 📜 Oishi S, Inoue Y, Miura J, et al. SeqSLAM++: View-based robot localization and navigation[J]. Robotics and Autonomous Systems, 2019, 112: 13-21.

30. Pyojin Kim（韩国首尔大学自主机器人实验室）

研究方向：视觉里程计，定位，AR/VR
👦 个人主页，谷歌学术
📜 平面 SLAM：ECCV 2018：Linear RGB-D SLAM for planar environments
📜 光照变化下的鲁棒 SLAM：ICRA 2017：Robust visual localization in changing lighting conditions
📜 线面 SLAM：CVPR 2018：Indoor RGB-D Compass from a Single Line and Plane
📜 博士学位论文：Low-Drift Visual Odometry for Indoor Robotics

31. 香港科技大学空中机器人实验室

研究方向：空中机器人在复杂环境下的自主运行，包括状态估计、建图、运动规划、多机器人协同以及低成本传感器和计算组件的实验平台开发。
实验室主页：http://uav.ust.hk/
发表论文：http://uav.ust.hk/publications/
👦 沈邵劼教授谷歌学术
代码公开地址：https://github.com/HKUST-Aerial-Robotics
📜 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 ）
📜 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 ）

32. 香港科技大学机器人与多感知实验室 RAM-LAB

研究方向：无人车；无人船；室内定位；机器学习。
实验室主页：https://www.ram-lab.com/
发表论文：https://www.ram-lab.com/publication/
👦 刘明教授谷歌学术
📜 Ye H, Chen Y, Liu M. Tightly coupled 3d lidar inertial odometry and mapping[C]//2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019: 3144-3150.（代码：https://github.com/hyye/lio-mapping ）
📜 Zhang J, Tai L, Boedecker J, et al. Neural slam: Learning to explore with external memory[J]. arXiv preprint arXiv:1706.09520, 2017.

33. 香港中文大学天石机器人实验室

研究方向：工业、物流、手术机器人，三维影像，机器学习
实验室主页：http://ri.cuhk.edu.hk/
👦 刘云辉教授：http://ri.cuhk.edu.hk/yhliu
👦 李浩昂：个人主页，谷歌学术
- 📜 Li H, Yao J, Bazin J C, et al. A monocular SLAM system leveraging structural regularity in Manhattan world[C]//2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2018: 2518-2525.
- 📜 Li H, Yao J, Lu X, et al. Combining points and lines for camera pose estimation and optimization in monocular visual odometry[C]//2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2017: 1289-1296.
- 📜 消失点检测：Lu X, Yaoy J, Li H, et al. 2-Line Exhaustive Searching for Real-Time Vanishing Point Estimation in Manhattan World[C]//Applications of Computer Vision (WACV), 2017 IEEE Winter Conference on. IEEE, 2017: 345-353.（代码：https://github.com/xiaohulugo/VanishingPointDetection ）
👦 郑帆：个人主页，谷歌学术
- 📜 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 ）
- 📜 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 ）

34. 浙江大学 CAD&CG 国家重点实验室

研究方向：SFM/SLAM，三维重建，增强现实
实验室主页：http://www.zjucvg.net/
Github 代码地址：https://github.com/zju3dv
👦 章国峰教授：个人主页，谷歌学术
📜 ICE-BA：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/zju3dv/EIBA ）
📜 RK-SLAM：Liu H, Zhang G, Bao H. Robust keyframe-based monocular SLAM for augmented reality[C]//2016 IEEE International Symposium on Mixed and Augmented Reality (ISMAR). IEEE, 2016: 1-10.（项目主页：http://www.zjucvg.net/rkslam/rkslam.html ）
📜 RD-SLAM：Tan W, Liu H, Dong Z, et al. Robust monocular SLAM in dynamic environments[C]//2013 IEEE International Symposium on Mixed and Augmented Reality (ISMAR). IEEE, 2013: 209-218.

35. 邹丹平（上海交通大学）

研究方向：视觉 SLAM，SFM，多源导航，微型无人机
👦 个人主页：http://drone.sjtu.edu.cn/dpzou/index.php ，谷歌学术
📜 Co-SLAM：Zou D, Tan P. Coslam: Collaborative visual slam in dynamic environments[J]. IEEE transactions on pattern analysis and machine intelligence, 2012, 35(2): 354-366.（代码：https://github.com/danping/CoSLAM ）
📜 StructSLAM：Zhou H, Zou D, Pei L, et al. StructSLAM: Visual SLAM with building structure lines[J]. IEEE Transactions on Vehicular Technology, 2015, 64(4): 1364-1375.（项目主页：http://drone.sjtu.edu.cn/dpzou/project/structslam.php ）
📜 StructVIO：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.

36. 布树辉教授（西北工业大学智能系统实验室）

研究方向：语义定位与建图、SLAM、在线学习与增量学习
👦 个人主页：http://www.adv-ci.com/blog/ 谷歌学术
布老师的课件：http://www.adv-ci.com/blog/course/
实验室 2018 年暑期培训资料：https://github.com/zdzhaoyong/SummerCamp2018
📜 开源的通用 SLAM 框架：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 ）
📜 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 ）
📜 Wang W, Zhao Y, Han P, et al. TerrainFusion: Real-time Digital Surface Model Reconstruction based on Monocular SLAM[C]//2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2019: 7895-7902.

+1 Cyrill Stachniss（德国波恩大学摄影测量与机器人实验室）

研究方向：概率机器人、SLAM、自主导航、视觉激光感知、场景分析与分配、无人飞行器
实验室主页：https://www.ipb.uni-bonn.de/
👦 个人主页：https://www.ipb.uni-bonn.de/people/cyrill-stachniss/ 谷歌学术
发表论文：https://www.ipb.uni-bonn.de/publications/
开源代码：https://github.com/PRBonn
📜 IROS 2019 激光语义 SLAM：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/ ）
Cyrill Stachniss 教授 SLAM 公开课：youtube ； bilibili
波恩大学另外一个智能自主系统实验室：http://www.ais.uni-bonn.de/research.html

+1 上海科技大学

Mobile Perception Lab：http://mpl.sist.shanghaitech.edu.cn/
👦 Laurent Kneip：个人主页；谷歌学术
📜 Zhou Y, Li H, Kneip L. Canny-vo: Visual odometry with rgb-d cameras based on geometric 3-d–2-d edge alignment[J]. IEEE Transactions on Robotics, 2018, 35(1): 184-199.
自主移动机器人实验室：https://robotics.shanghaitech.edu.cn/zh
👦 Sören Schwertfeger：个人主页；谷歌学术
📜 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 ）

+1 美国密歇根大学机器人研究所

学院官网：https://robotics.umich.edu/
研究方向：https://robotics.umich.edu/research/focus-areas/
感知机器人实验室（PeRL）
- 实验室主页：http://robots.engin.umich.edu/About/
- 👦 Ryan M. Eustice 谷歌学术
- 📜 激光雷达数据集 Pandey G, McBride J R, Eustice R M. Ford campus vision and lidar data set[J]. The International Journal of Robotics Research, 2011, 30(13): 1543-1552. | 数据集
APRIL robotics lab
- 实验室主页：https://april.eecs.umich.edu/
- 👦 Edwin Olson 个人主页 | 谷歌学术
- 📜 Olson E. AprilTag: A robust and flexible visual fiducial system[C]//2011 IEEE International Conference on Robotics and Automation. IEEE, 2011: 3400-3407. | 代码
- 📜 Wang X, Marcotte R, Ferrer G, et al. ApriISAM: Real-time smoothing and mapping[C]//2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2018: 2486-2493. | 代码

+1 瑞士苏黎世联邦理工自主系统实验室

研究方向：复杂多样环境中自主运行的机器人和智能系统
实验室主页：https://asl.ethz.ch/
发表论文：https://asl.ethz.ch/publications-and-sources/publications.html
youtube | Github
👦 Cesar Cadena 个人主页
📜 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
📜 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
📜 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

+1 美国麻省理工学院 Robust Robotics Group

研究方向：MAV 导航与控制；人机交互的自然语言理解；自主海洋机器人的语义理解
实验室主页：http://groups.csail.mit.edu/rrg/index.php
👦 Nicholas Roy：Google Scholar
📜 Greene W N, Ok K, Lommel P, et al. Multi-level mapping: Real-time dense monocular SLAM[C]//2016 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2016: 833-840. video
📜 ICRA 2020 Metrically-Scaled Monocular SLAM using Learned Scale Factors." International Conference on Robotics and Automation | video
📜 ICRA 2019 Robust Object-based SLAM for High-speed Autonomous Navigation

+1 瑞士苏黎世联邦理工 Vision for Robotics Lab

研究方向：机器人视觉，无人机，自主导航，多机器人协同
实验室主页：https://v4rl.ethz.ch/the-group.html
👦 Margarita Chli：个人主页 | Google Scholar
📜 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. code | video
📜 Bartolomei L, Karrer M, Chli M. Multi-robot Coordination with Agent-Server Architecture for Autonomous Navigation in Partially Unknown Environments[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2020)(virtual). 2020. code | video
📜 Schmuck P, Chli M. Multi-uav collaborative monocular slam[C]//2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2017: 3863-3870.

+1 谢立华教授（南洋理工大学）

研究方向：控制，多智能体，定位
个人主页：https://personal.ntu.edu.sg/elhxie/research.html | Google Scholar
👦 Wang Han：个人主页 | Github
📜 Wang H, Wang C, Xie L. Intensity scan context: Coding intensity and geometry relations for loop closure detection[C]//2020 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2020: 2095-2101. | Code
📜 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. | Code
📜 Wang C, Yuan J, Xie L. Non-iterative SLAM[C]//2017 18th International Conference on Advanced Robotics (ICAR). IEEE, 2017: 83-90.

3. SLAM 学习资料

这一部分的内容不太完整，陆续丰富，欢迎补充文章来源地址https://www.toymoban.com/news/detail-774085.html

3.1 国内资料

1) SLAMcn：http://www.slamcn.org/index.php/
2) SLAM 最新研究更新 Recent_SLAM_Research ：https://github.com/YiChenCityU/Recent_SLAM_Research
3) 西北工大智能系统实验室 SLAM 培训：https://github.com/zdzhaoyong/SummerCamp2018
- 布树辉老师课件：http://www.adv-ci.com/blog/course/
4) IROS 2019 视觉惯导导航的挑战与应用研讨会：http://udel.edu/~ghuang/iros19-vins-workshop/index.html
5) 泡泡机器人 VIO 相关资料：https://github.com/PaoPaoRobot/Awesome-VIO
6) 崔华坤：主流 VIO 论文推导及代码解析：https://github.com/StevenCui/VIO-Doc
7) 李言：SLAM 中的几何与学习方法
8) 黄山老师状态估计视频：bilibili
9) 谭平老师-SLAM 6小时课程：bilibili
10) 2020 年 SLAM 技术及应用暑期学校：视频-bilibili | 课件

3.2 国外资料

1) 事件相机相关研究与发展：https://github.com/uzh-rpg/event-based_vision_resources
2) 加州大学圣地亚哥分校语境机器人研究所 Nikolay Atanasov 教授机器人状态估计与感知课程 ppt：https://natanaso.github.io/ece276a2019/schedule.html
3) 波恩大学 Mobile Sensing and Robotics Course 公开课：youtube ，bilibili