感谢SLAMer前辈们不断的拼搏与进取，才有了现在的丰富的学习资料！
以下是至今SLAM开源代码的资料汇总，后续将会更新主流slam开源代码的注释版本，希望对研究SLAM的同学们有帮助。
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
作者其他研究：http://www.robots.ox.ac.uk/~gk/publications.html
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
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
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.
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
SVO
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.
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
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
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
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
Kintinuous
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
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
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
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：https://github.com/HeYijia/ORB_SLAM2
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
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
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
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
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
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
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
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
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.
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.
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.
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
maplab（视惯建图框架,多会话建图，地图合并，视觉惯性批处理优化和闭环）
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
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
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
FlashFusion
论文：Han L, Fang L. FlashFusion: Real-time Globally Consistent Dense 3D Reconstruction using CPU Computing[C]. RSS, 2018.
代码：https://github.com/lhanaf/FlashFusion
probabilistic_mapping（单目概率稠密重建）
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.
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
ICE-BA (代码可读性太差了)
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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/
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
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
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
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
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
LARVIO（多状态约束卡尔曼滤波的单目 VIO，可以在线标定imu）
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
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
PVIO
论文：Robust and Efficient Visual-Inertial Odometry with Multi-plane PriorsJinyu Li, Bangbang Yang, Kai Huang, Guofeng Zhang, and Hujun Bao*PRCV 2019, LNCS 11859, pp. 283–295, 2019.
代码：https://github.com/zju3dv/PVIO
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
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
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
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
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
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
相关研究：基于超像素的单目 SLAM：Using Superpixels in Monocular SLAM ICRA 2014
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
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
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
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
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/
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
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
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
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
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
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
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
DeepFactors（实时的概率单目稠密 SLAM）
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.
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
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