83 项开源视觉 SLAM 方案够你用了吗？

作者：吴艳敏

来源：83 项开源视觉 SLAM 方案够你用了吗？

前言

1. 本文由知乎作者小吴同学同步发布于https://zhuanlan.zhihu.com/p/115599978/并持续更新。

2. 本文简单将各种开源视觉SLAM方案分为以下 7 类（固然有不少文章无法恰当分类）：

·Geometric SLAM

·Semantic / Learning SLAM

·Multi-Landmarks / Object SLAM

·VIO / VISLAM

·Dynamic SLAM

·Mapping

·Optimization

3. 由于本人自 2019 年 3 月开始整理，所以以下的代码除了经典的框架之外基本都集中在 19-20 年；此外个人比较关注 VO、物体级 SLAM 和多路标 SLAM，所以以下内容收集的也不完整，无法涵盖视觉 SLAM 的所有研究，仅作参考。

 

一、Geometric SLAM（20 项）

这一类是传统的基于特征点、直接法或半直接法的 SLAM，虽说传统，但 2019 年也新诞生了 9 个开源方案。

1. PTAM

论文：Klein G, Murray D. Parallel tracking and mapping for small AR workspaces[C]//Mixed andAugmented Reality, 2007. ISMAR 2007. 6th IEEE and ACM International Symposiumon. 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 AutonomousSystems, 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 throughconstrained-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 patternanalysis 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: Anopen-source slam system for monocular, stereo, and rgb-d cameras[J]. IEEETransactions 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 AccurateFeature-Based Monocular SLAM[C]//Robotics: Science and Systems. 2015,2015.

VIORB：Mur-Artal R, Tardós J D. Visual-inertialmonocular SLAM with map reuse[J]. IEEE Robotics and AutomationLetters, 2017, 2(2): 796-803.

多地图：Elvira R, Tardós J D, Montiel J M M. ORBSLAM-Atlas: arobust 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

5. DSO

论文：Engel J, Koltun V, Cremers D. Direct sparseodometry[J]. IEEE transactions on pattern analysis and machineintelligence, 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 withstereo cameras[C]//Proceedings of the IEEE International Conference onComputer Vision. 2017: 3903-3911.

VI-DSO：Von Stumberg L, Usenko V, Cremers D. Direct sparsevisual-inertial odometry using dynamic marginalization[C]//2018 IEEEInternational 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: Directsparse odometry with loop closure[C]//2018 IEEE/RSJ InternationalConference 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]//Europeanconference 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 visualSLAM for RGB-D cameras[C]//2013 IEEE/RSJ International Conferenceon 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 IEEEinternational 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 IEEEinternational 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 IEEEinternational conference on robotics and automation (ICRA). IEEE, 2014:15-22.

代码：https://github.com/uzh-rpg/rpg_svo

Forster C, ZhangZ, Gassner M, et al. SVO: Semidirect visual odometry for monocular andmulticamera systems[J]. IEEE Transactions on Robotics, 2016,33(2): 249-265.

10. DSM

论文：Zubizarreta J, Aguinaga I, Montiel J M M. Direct sparsemapping[J]. arXiv preprint arXiv:1904.06577, 2019.

代码：https://github.com/jzubizarreta/dsm

11. openvslam

论文：Sumikura S,Shibuya M, Sakurada K. OpenVSLAM: A Versatile Visual SLAM Framework[C]//Proceedingsof the 27th ACM International Conference on Multimedia. 2019: 2292-2295.

代码：https://github.com/xdspacelab/openvslam

12. se2lam（地面车辆位姿估计的视觉里程计）

论文：Zheng F, Liu Y H. Visual-OdometricLocalization and Mapping for Ground Vehicles Using SE (2)-XYZ Constraints[C]//2019International 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-basedground vehicle motion estimation with se (2)-constrained se (3) poses[J]. IEEEtransactions on cybernetics, 2018, 49(7): 2652-2663.

代码：https://github.com/izhengfan/se2clam

13. GraphSfM（基于图的并行大尺度 SFM）

论文：Chen Y, Shen S,Chen Y, et al. Graph-BasedParallel Large Scale Structure from Motion[J]. arXivpreprint 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-Coupledsemi-direct monocular SLAM[J]. IEEE Robotics and AutomationLetters, 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, deCroon G. Stability-based scale estimation for monocular SLAM[J]. IEEERobotics and Automation Letters, 2018, 3(2): 780-787.

15. RESLAM（基于边的 SLAM）

论文：Schenk F, Fraundorfer F. RESLAM: Areal-time robust edge-based SLAM system[C]//2019 International Conference onRobotics and Automation (ICRA). IEEE, 2019: 154-160.

代码：https://github.com/fabianschenk/RESLAM

16. scale_optimization（将单目 DSO 拓展到双目）

论文：Mo J, Sattar J. ExtendingMonocular 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]//Proceedingsof 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]//Proceedingsof 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 groundvehicles 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. ContinuousDirect Sparse Visual Odometry from RGB-D Images[J]. arXivpreprint arXiv:1904.02266, 2019.

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

 

二、Semantic / Learning SLAM（12 项）

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

21. MsakFusion

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

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

22. SemanticFusion

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

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

23. semantic_3d_mapping

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

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

24. Kimera（实时度量与语义定位建图开源库）

论文：Rosinol A, AbateM, Chang Y, et al. Kimera: anOpen-Source Library for Real-Time Metric-Semantic Localization and Mapping[J]. arXivpreprint arXiv:1910.02490, 2019.

代码：https://github.com/MIT-SPARK/Kimera

25. NeuroSLAM（脑启发式 SLAM）

论文：Yu F, Shang J, Hu Y, et al. NeuroSLAM: a brain-inspired SLAM system for 3Denvironments[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

26. 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]. arXivpreprint arXiv:1910.10672, 2019.

代码（预计 20 年 4 月放出）：https://github.com/montrealrobotics/gradSLAM

27. 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

28. SIVO（语义辅助特征选择）

论文：Ganti P, Waslander S. NetworkUncertainty Informed Semantic Feature Selection for Visual SLAM[C]//2019 16thConference on Computer and Robot Vision (CRV). IEEE, 2019: 121-128.

代码：https://github.com/navganti/SIVO

29. 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 usingProximity Graphs. pp. 378-385, The 2019 IEEE/RSJ International Conferenceon Intelligent Robots and Systems (IROS2019)

代码：https://github.com/AnshanTJU/FILD

30. 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 VisualSLAM System[J]. Journal of Intelligent & Robotic Systems, 2019:1-10.

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

31. Map Slammer（单目深度估计 + SLAM）

论文：Torres-Camara J M, Escalona F, Gomez-DonosoF, et al. Map Slammer: Densifying Scattered KSLAM 3D Maps withEstimated Depth[C]//Iberian Robotics conference. Springer, Cham, 2019:563-574.

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

32. NOLBO（变分模型的概率 SLAM）

论文：Yu H, Lee B. Not Only LookBut Observe: Variational Observation Model of Scene-Level 3D Multi-ObjectUnderstanding for Probabilistic SLAM[J]. arXiv preprint arXiv:1907.09760, 2019.

代码：https://github.com/bogus2000/NOLBO

 

三、Multi-Landmarks / Object SLAM（12 项）

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

33. PL-SVO（点线 SVO）

论文：Gomez-Ojeda R, Briales J, Gonzalez-JimenezJ. PL-SVO: Semi-direct Monocular Visual Odometry by combiningpoints 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

34. stvo-pl（双目点线 VO）

论文：Gomez-Ojeda R, Gonzalez-Jimenez J. Robust stereo visual odometry through a probabilisticcombination of points and line segments[C]//2016 IEEE International Conferenceon Robotics and Automation (ICRA). IEEE, 2016: 2521-2526.

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

35. PL-SLAM（点线 SLAM）

论文：Gomez-Ojeda R, Zuñiga-Noël D, Moreno F A,et al. PL-SLAM: aStereo SLAM System through the Combination of Points and Line Segments[J]. arXivpreprint 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 ofpoints and line segments[J]. IEEE Transactions on Robotics, 2019,35(3): 734-746.

36. PL-VIO

论文：He Y, Zhao J, Guo Y, et al. PL-VIO:Tightly-coupled monocular visual–inertial odometry using point and linefeatures[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

37. 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]. IEEEAccess, 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]. IEEETransactions on Robotics, 2019, 35(4): 999-1013. + 浙大的 Zuo X, Xie X, Liu Y, et al. Robust visualSLAM with point and line features[C]//2017 IEEE/RSJ InternationalConference on Intelligent Robots and Systems (IROS). IEEE, 2017:1775-1782.

38. PlaneSLAM

论文：Wietrzykowski J. On the representation of planes for efficient graph-basedslam with high-level features[J]. Journal of Automation MobileRobotics 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

39. Eigen-Factors（特征因子平面对齐）

论文：Ferrer G. Eigen-Factors: Plane Estimation for Multi-Frame andTime-Continuous Point Cloud Alignment[C]//2019 IEEE/RSJ InternationalConference on Intelligent Robots and Systems (IROS). IEEE, 2019:1278-1284.

代码：https://gitlab.com/gferrer/eigen-factors-iros2019

40. PlaneLoc

论文：Wietrzykowski J, Skrzypczyński P. PlaneLoc:Probabilistic global localization in 3-D using local planar features[J]. Roboticsand Autonomous Systems, 2019, 113: 160-173.

代码：https://github.com/LRMPUT/PlaneLoc

41. 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]//2016IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).IEEE, 2016: 1222-1229.

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

42. Object SLAM

论文：Mu B, Liu S Y, Paull L, et al. Slam withobjects using a nonparametric pose graph[C]//2016 IEEE/RSJ InternationalConference on Intelligent Robots and Systems (IROS). IEEE, 2016:4602-4609.

代码：https://github.com/BeipengMu/objectSLAM

43. voxblox-plusplus（物体级体素建图）

论文：Grinvald M, Furrer F, Novkovic T, et al. Volumetricinstance-aware semantic mapping and 3D object discovery[J]. IEEERobotics and Automation Letters, 2019, 4(3): 3037-3044.

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

44. 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

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. RobustObject-based SLAM for High-speed Autonomous Navigation[C]//2019International Conference on Robotics and Automation (ICRA). IEEE, 2019:669-675.

Li J, Meger D,Dudek G. SemanticMapping for View-Invariant Relocalization[C]//2019International 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]. IEEERobotics and Automation Letters, 2018, 4(1): 1-8.

 

四、VIO / VISLAM（10 项）

在传感器融合方面只关注了视觉 + 惯导，其他传感器像 LiDAR，GPS 关注较少（SLAM 太复杂啦 -_-! ）。视惯融合的新工作也相对较少，基本一些经典的方案就够用了。

45. msckf_vio

论文：Sun K, Mohta K, Pfrommer B, et al. Robust stereovisual inertial odometry for fast autonomous flight[J]. IEEERobotics and Automation Letters, 2018, 3(2): 965-972.

代码：https://github.com/KumarRobotics/msckf_vio

46. rovio

论文：Bloesch M, Omari S, Hutter M, et al. Robust visual inertial odometry using a direct EKF-basedapproach[C]//2015 IEEE/RSJ international conference onintelligent robots and systems (IROS). IEEE, 2015: 298-304.

代码：https://github.com/ethz-asl/rovio

47. R-VIO

论文：Huai Z, Huang G. Robocentricvisual-inertial odometry[C]//2018 IEEE/RSJ InternationalConference on Intelligent Robots and Systems (IROS). IEEE, 2018:6319-6326.

代码：https://github.com/rpng/R-VIO

48. okvis

论文：Leutenegger S, Lynen S, Bosse M, et al. Keyframe-based visual–inertial odometry using nonlinearoptimization[J]. The International Journal of Robotics Research, 2015,34(3): 314-334.

代码：https://github.com/ethz-asl/okvis

49. VIORB

论文：Mur-Artal R, Tardós J D. Visual-inertialmonocular SLAM with map reuse[J]. IEEE Robotics and AutomationLetters, 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）

50. VINS-mono

论文：Qin T, Li P, Shen S. Vins-mono: Arobust and versatile monocular visual-inertial state estimator[J]. IEEETransactions 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

51. VINS-RGBD

论文：Shan Z, Li R, Schwertfeger S. RGBD-InertialTrajectory Estimation and Mapping for Ground Robots[J]. Sensors, 2019,19(10): 2251.

代码：https://github.com/STAR-Center/VINS-RGBD

52. Open-VINS

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

代码：https://github.com/rpng/open_vins

53. versavis（多功能的视惯传感器系统）

论文：Tschopp F, RinerM, Fehr M, et al. VersaVIS—AnOpen Versatile Multi-Camera Visual-Inertial Sensor Suite[J]. Sensors, 2020,20(5): 1439.

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

54. 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-basedvisual–inertial navigation[J]. The International Journal ofRobotics Research, 2018.

代码：https://github.com/rpng/cpi

 

五、Dynamic SLAM（5 项）

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

55. DynamicSemanticMapping（动态语义建图）

论文：Kochanov D, Ošep A, Stückler J, et al. Scene flow propagation for semantic mapping and objectdiscovery 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

56. DS-SLAM（动态语义 SLAM）

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

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

57. 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 IEEEInternational Conference on Robotics and Automation (ICRA). IEEE, 2017:4471-4478.

代码：https://github.com/martinruenz/co-fusion

58. DynamicFusion

论文：Newcombe R A, Fox D, Seitz S M. Dynamicfusion: Reconstruction and tracking of non-rigidscenes in real-time[C]//Proceedings of the IEEE conference oncomputer vision and pattern recognition. 2015: 343-352.

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

59. ReFusion（动态场景利用残差三维重建）

论文：Palazzolo E, Behley J, Lottes P, et al. ReFusion: 3DReconstruction in Dynamic Environments for RGB-D Cameras Exploiting Residuals[J]. arXivpreprint arXiv:1905.02082, 2019.

代码：https://github.com/PRBonn/refusion

 

六、Mapping（18 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。

60. 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 3dreconstruction with loop closure[J]. arXiv preprint arXiv:1708.00783, 2017.

代码：https://github.com/victorprad/InfiniTAM

61. BundleFusion

论文：Dai A, Nießner M, Zollhöfer M, et al. Bundlefusion:Real-time globally consistent 3d reconstruction using on-the-fly surfacereintegration[J]. ACM Transactions on Graphics (TOG), 2017,36(4): 76a.

代码：https://github.com/niessner/BundleFusion

62. KinectFusion

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

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

63. ElasticFusion

论文：Whelan T, Salas-Moreno R F, Glocker B, etal. ElasticFusion: Real-time dense SLAM and light sourceestimation[J]. The International Journal of Robotics Research, 2016,35(14): 1697-1716.

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

64. Kintinuous

ElasticFusion 同一个团队的工作，帝国理工 Stefan Leutenegger

论文：Whelan T, Kaess M, Johannsson H, et al. Real-time large-scale dense RGB-D SLAM with volumetricfusion[J]. The International Journal of Robotics Research, 2015,34(4-5): 598-626.

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

65. ElasticReconstruction

论文：Choi S, Zhou Q Y, Koltun V. Robust reconstruction of indoor scenes[C]//Proceedingsof the IEEE Conference on Computer Vision and Pattern Recognition. 2015:5556-5565.

代码：https://github.com/qianyizh/ElasticReconstruction

66. 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

67. 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 simultaneouslocalization and mapping library for large‐scale and long‐term online operation[J]. Journal ofField Robotics, 2019, 36(2): 416-446.

代码：https://github.com/introlab/rtabmap

68. RobustPCLReconstruction（户外稠密重建）

论文：Lan Z, Yew Z J, Lee G H. Robust Point Cloud Based Reconstruction of Large-ScaleOutdoor Scenes[C]//Proceedings of the IEEE Conference on ComputerVision and Pattern Recognition. 2019: 9690-9698.

代码：https://github.com/ziquan111/RobustPCLReconstruction

69. plane-opt-rgbd（室内平面重建）

论文：Wang C, Guo X. Efficient Plane-Based Optimization of Geometry and Texturefor Indoor RGB-D Reconstruction[C]//Proceedings of the IEEE Conferenceon Computer Vision and Pattern Recognition Workshops. 2019: 49-53.

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

70. DenseSurfelMapping（稠密表面重建）

论文：Wang K, Gao F, Shen S. Real-timescalable dense surfel mapping[C]//2019 International Conference onRobotics and Automation (ICRA). IEEE, 2019: 6919-6925.

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

71. surfelmeshing（网格重建）

论文：Schöps T, Sattler T, Pollefeys M. Surfelmeshing:Online surfel-based mesh reconstruction[J]. IEEE Transactions on PatternAnalysis and Machine Intelligence, 2019.

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

72. DPPTAM（单目稠密重建）

论文：Concha Belenguer A, Civera Sancho J. DPPTAM: Dense piecewise planar tracking and mapping from amonocular 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：UsingSuperpixels in Monocular SLAM ICRA 2014 ；谷歌学术

73. 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 usingvisual-inertial fusion[C]//2017 IEEE International Conference onRobotics and Automation (ICRA). IEEE, 2017: 4552-4559.

代码：https://github.com/dvorak0/VI-MEAN

74. REMODE（单目概率稠密重建）

论文：Pizzoli M, Forster C, Scaramuzza D. REMODE: Probabilistic, monocular dense reconstruction inreal time[C]//2014 IEEE International Conference on Robotics andAutomation (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_SLAM2https://github.com/ayushgaud/ORB_SLAM2

75. 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]. arXivpreprint 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 representationfor dense visual SLAM[C]//Proceedings of the IEEE conference oncomputer vision and pattern recognition. 2018: 2560-2568.

76. 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. Probabilisticdense reconstruction from a moving camera[C]//2018IEEE/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‐timedense mapping for online processing and navigation[J]. Journal ofField Robotics, 2019, 36(5): 1004-1036.

77. ORB-SLAM2 单目半稠密建图

论文：Mur-Artal R, Tardós J D. Probabilistic Semi-Dense Mapping from Highly AccurateFeature-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. Incremental3d line segment extraction from semi-dense slam[C]//2018 24thInternational 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

 

七、Optimization（6 项）

个人感觉优化可能是 SLAM 中最难的一部分了吧 +_+ ，我们一般都是直接用现成的因子图、图优化方案，要创新可不容易，分享山川小哥d的入坑指南https://zhuanlan.zhihu.com/p/53972892。

78. 后端优化库

GTSAM：https://github.com/borglab/gtsam

g2o：https://github.com/RainerKuemmerle/g2o

ceres：http://ceres-solver.org/

79. ICE-BA

论文：Liu H, Chen M, Zhang G, et al. Ice-ba: Incremental, consistent and efficient bundleadjustment for visual-inertial slam[C]//Proceedings of the IEEE Conferenceon Computer Vision and Pattern Recognition. 2018: 1974-1982.

代码：https://github.com/baidu/ICE-BA

80. minisam（因子图最小二乘优化框架）

论文：Dong J, Lv Z. miniSAM: AFlexible Factor Graph Non-linear Least Squares Optimization Framework[J]. arXivpreprint arXiv:1909.00903, 2019.

代码：https://github.com/dongjing3309/minisam

81. 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 Primitivesin Graph-SLAM Optimization[J]. IEEE Robotics and AutomationLetters, 2019, 4(3): 2738-2745.

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

82. MH-iSAM2（SLAM 优化器）

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

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

83. MOLA（用于定位和建图的模块化优化框架）

论文：Blanco-Claraco J L. A ModularOptimization Framework for Localization and Mapping[J]. Proc. ofRobotics: Science and Systems (RSS), FreiburgimBreisgau, Germany, 2019,2.

代码：https://github.com/MOLAorg/mola