人工智能 | 开源视觉里程计、SLAM、VIO、Net-SLAM、激光SLAM、语义SLAM、数据集（总结）
博主github：https://github.com/MichaelBeechan
博主CSDN：https://blog.csdn.net/u011344545
项目工程：https://github.com/MichaelBeechan/VO-SLAM-Review
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Visual Odometry（VO）-SLAM-ReviewSLAM is mainly divided into two parts: the front end and the back end. The front end is the visual odometer (VO), which roughly estimates the motion of the camera based on the information of adjacent images and provides a good initial value for the back end.The implementation methods of VO can be divided into two categories according to whether features are extracted or not: feature point-based methods, and direct methods without feature points. VO based on feature points is stable and insensitive to illumination and dynamic objects
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github：https://github.com/MichaelBeechan
CSDN：https://blog.csdn.net/u011344545
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SALM review paper download：
https://download.csdn.net/download/u011344545/10850261
1、Visual Odometry or VSLAM 2、Visual Inertial Odometry or VIO-SLAM 3、Based CNN（Net VO or Net VSLAM） 4、Lidar Visual odometry or Lidar SLAM 5、Semanitc SLAM 6、Datasets 7、Libraries OF-VO：Robust and Efficient Stereo Visual Odometry Using Points and Feature Optical FlowCode：https://github.com/MichaelBeechan/MyStereoLibviso2
SLAMBookPaper：14 Lectures on Visual SLAM: From Theory to Practice,
Code：https://github.com/gaoxiang12/slambook
SLAMBook2Code：https://github.com/gaoxiang12/slambook2
SVO: Fast Semi-Direct Monocular Visual OdometryPaper：http://rpg.ifi.uzh.ch/docs/ICRA14_Forster.pdf
Video: http://youtu.be/2YnIMfw6bJY
Code：https://github.com/uzh-rpg/rpg_svo
Robust Odometry Estimation for RGB-D Cameras Real-Time Visual Odometry from Dense RGB-D ImagesPaper：http://www.cs.nuim.ie/research/vision/data/icra2013/Whelan13icra.pdf
Code：https://github.com/tum-vision/dvo
PTAM：Parallel Tracking and Mapping for Small AR WorkspacesPaper：https://cse.sc.edu/~yiannisr/774/2015/ptam.pdf
http://www.robots.ox.ac.uk/ActiveVision/Papers/klein_murray_ismar2007/klein_murray_ismar2007.pdf
Code：https://github.com/Oxford-PTAM/PTAM-GPL
se2lam：Visual-Odometric Localization and Mapping for Ground Vehicles Using SE (2)-XYZ ConstraintsPaper：https://ieeexplore.ieee.org/abstract/document/8793928
Code：https://github.com/izhengfan/se2lam
se2clam：Odometry-vision-based ground vehicle motion estimation with se (2)-constrained se (3) posesPaper：https://ieeexplore.ieee.org/abstract/document/8357438/
Code：https://github.com/izhengfan/se2clam
ORBSLAMCode1：https://github.com/raulmur/ORB_SLAM2
Code2：https://github.com/raulmur/ORB_SLAM
A ROS Implementation of the Mono-Slam AlgorithmPaper：https://www.researchgate.net/publication/269200654_A_ROS_Implementation_of_the_Mono-Slam_Algorithm
Code：https://github.com/rrg-polito/mono-slam
DTAM: Dense tracking and mapping in real-timePaper：https://ieeexplore.ieee.org/document/6126513
Code：https://github.com/anuranbaka/OpenDTAM
PTAM： Parallel tracking and mapping for small AR workspacesPaper：http://www.robots.ox.ac.uk/ActiveVision/Publications/klein_murray_ismar2007/klein_murray_ismar2007.pdf
Code：https://github.com/Oxford-PTAM/PTAM-GPL
S-PTAM: Stereo Parallel Tracking and MappingPaper：https://www.researchgate.net/publication/316286318_S-PTAM_Stereo_Parallel_Tracking_and_Mapping
Code：https://github.com/lrse/sptam
stability_scale：Stability-based Scale Estimation for Monocular SLAMPaper：https://www.researchgate.net/publication/322260802_Stability-based_Scale_Estimation_for_Monocular_SLAM
Code：https://github.com/sunghoon031/stability_scale
LCSD_SLAM：Loosely-Coupled Semi-Direct Monocular SLAMPaper：https://arxiv.org/pdf/1807.10073.pdf
Code：https://github.com/sunghoon031/LCSD_SLAM
GraphSfM：Graph-Based Parallel Large Scale Structure from MotionPaper：https://arxiv.org/pdf/1912.10659.pdf
Code：https://github.com/AIBluefisher/GraphSfM
LSD-SLAM: Large-Scale Direct Monocular SLAMPaper：http://pdfs.semanticscholar.org/c13c/b6dfd26a1b545d50d05b52c99eb87b1c82b2.pdf
https://vision.in.tum.de/research/vslam/lsdslam
Code：https://github.com/tum-vision/lsd_slam
BadSLAM：BAD SLAM: Bundle Adjusted Direct RGB-D SLAMPaper：http://openaccess.thecvf.com/content_CVPR_2019/html/Schops_BAD_SLAM_Bundle_Adjusted_Direct_RGB-D_SLAM_CVPR_2019_paper.html
Code：https://link.zhihu.com/?target=https%3A//github.com/ETH3D/badslam
RGBD-Odometry (Visual Odometry based RGB-D images) Real-Time Visual Odometry from Dense RGB-D ImagesCode：https://github.com/tzutalin/OpenCV-RgbdOdometry
Paper：http://www.computer.org/csdl/proceedings/iccvw/2011/0063/00/06130321.pdf
Py-MVO: Monocular Visual Odometry using PythonCode：https://github.com/Transportation-Inspection/visual_odometry
Video：https://www.youtube.com/watch?v=E8JK19TmTL4&feature=youtu.be
Stereo-Odometry-SOFTMATLAB Implementation of Visual Odometry using SOFT algorithm
Code：https://github.com/Mayankm96/Stereo-Odometry-SOFT
Paper：https://ieeexplore.ieee.org/document/7324219
GF_ORB_SLAM：Good Feature Matching: Towards Accurate, Robust VO/VSLAM with Low LatencyPaper：https://arxiv.org/pdf/2001.00714.pdf
Code：https://github.com/ivalab/GF_ORB_SLAM
monoVO-pythonCode1：https://github.com/uoip/monoVO-pythone：https://github.com/uoip/monoVO-python
Code2：https://github.com/yueying/LearningVO
DVO：Robust Odometry Estimation for RGB-D CamerasCode：https://github.com/tum-vision/dvo
https://vision.in.tum.de/data/software/dvo
Paper：https://www.researchgate.net/publication/221430091_Real-time_visual_odometry_from_dense_RGB-D_images
Dense Visual Odometry and SLAM (dvo_slam)Code：https://github.com/tum-vision/dvo_slam
https://vision.in.tum.de/data/software/dvo
Paper：https://www.researchgate.net/publication/261353146_Dense_visual_SLAM_for_RGB-D_cameras
REVO：Robust Edge-based Visual Odometry Combining Edge Images and Depth Maps for Robust Visual Odometry Robust Edge-based Visual Odometry using Machine-Learned EdgesCode：https://github.com/fabianschenk/REVO
Paper：https://graz.pure.elsevier.com/
xivoX Inertial-aided Visual Odometry
Code：https://github.com/ucla-vision/xivo
Paper：XIVO: X Inertial-aided Visual Odometry and Sparse Mapping
PaoPaoRobotCode：https://github.com/PaoPaoRobot
ygz-slamCode：https://github.com/PaoPaoRobot/ygz-slam
https://github.com/gaoxiang12/ygz-stereo-inertial
https://github.com/gaoxiang12/ORB-YGZ-SLAM
https://www.ctolib.com/generalized-intelligence-GAAS.html#5-ygz-slam
RTAB MAP RTAB MAP - Real-Time Appearance-Based Mapping. Available on ROSOnline Global Loop Closure Detection for Large-Scale Multi-Session Graph-Based SLAM, 2014
Appearance-Based Loop Closure Detection for Online Large-Scale and Long-Term Operation, 2013
Code：https://github.com/slightech
Kintinuous Real-time Large Scale Dense RGB-D SLAM with Volumetric Fusion Deformation-based Loop Closure for Large Scale Dense RGB-D SLAM Robust Real-Time Visual Odometry for Dense RGB-D Mapping Kintinuous: Spatially Extended KinectFusion A method and system for mapping an environmentCode：https://github.com/mp3guy/Kintinuous
ElasticFusion ElasticFusion: Dense SLAM Without A Pose Graph ElasticFusion: Real-Time Dense SLAM and Light Source EstimationPaper：http://www.thomaswhelan.ie/Whelan16ijrr.pdf http://thomaswhelan.ie/Whelan15rss.pdf
Code：https://github.com/mp3guy/ElasticFusion
Co-Fusion：Real-time Segmentation, Tracking and Fusion of Multiple ObjectsPaper：http://visual.cs.ucl.ac.uk/pubs/cofusion/index.html
R-VIO：Robocentric Visual-Inertial Odometry(Kimera-VIO is a Visual Inertial Odometry pipeline for accurate State Estimation from Stereo + IMU data.)
Code：https://github.com/rpng/R-VIO
Paper：https://arxiv.org/abs/1805.04031
Kimera-VIO: Open-Source Visual Inertial OdometryCode：https://github.com/MIT-SPARK/Kimera-VIO
Paper：https://arxiv.org/abs/1910.02490
Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping
ADVIO: An Authentic Dataset for Visual-Inertial OdometryCode：https://github.com/AaltoVision/ADVIO
Paper：https://arxiv.org/abs/1807.09828
Data：https://zenodo.org/record/1476931#.XgCvYVIza00
MSCKF_VIO：Robust Stereo Visual Inertial Odometry for Fast Autonomous FlightPaper：https://arxiv.org/abs/1712.00036
Code：https://github.com/KumarRobotics/msckf_vio
VI-MEAN： Real-time monocular dense mapping on aerial robots using visual-inertial fusionPaper：https://github.com/dvorak0/VI-MEAN/raw/master/ICRA17_1095_MS.pdf
Code：https://github.com/dvorak0/VI-MEAN
Kimera-VIO: Open-Source Visual Inertial OdometryKimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping
Code：https://github.com/MIT-SPARK/Kimera-VIO
Paper：https://arxiv.org/abs/1910.02490
LIBVISO2: C++ Library for Visual Odometry 2Paper：http://www.cvlibs.net/software/libviso/
Code：https://github.com/srv/viso2
Stereo Visual SLAM for Mobile Robots Navigation A constant-time SLAM back-end in the continuum between global mapping and submapping: application to visual stereo SLAMPaper：http://mapir.uma.es/famoreno/papers/thesis/FAMD_thesis.pdf
Code：https://github.com/famoreno/stereo-vo
Combining Edge Images and Depth Maps for Robust Visual Odometry Robust Edge-based Visual Odometry using Machine-Learned Edges(REVO)Paper：https://graz.pure.elsevier.com/
Code：https://github.com/fabianschenk/REVO
HKUST Aerial Robotics Group VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State EstimatorPaper：https://arxiv.org/pdf/1708.03852.pdf
Code：https://github.com/HKUST-Aerial-Robotics/VINS-Mono
VINS-Fusion:Online Temporal Calibration for Monocular Visual-Inertial SystemsPaper：https://arxiv.org/pdf/1808.00692.pdf
Code：https://github.com/HKUST-Aerial-Robotics/VINS-Fusion
Monocular Visual-Inertial State Estimation for Mobile Augmented RealityPaper：https://ieeexplore.ieee.org/document/8115400
Code：https://github.com/HKUST-Aerial-Robotics/VINS-Mobile
Computer Vision Group TUM Department of Informatics Technical University of Munich DSO: Direct Sparse OdometryCode：https://github.com/JingeTu/StereoDSO
VIDSO：Visual-Inertial DSO：https://vision.in.tum.de/research/vslam/vi-dso DVSO：https://vision.in.tum.de/research/vslam/dvso LDSO：DSO with Loop-closure and Sim(3) pose graph optimization：https://vision.in.tum.de/research/vslam/ldso DSM：Direct Sparse MappingPaper：https://arxiv.org/pdf/1904.06577.pdf
Code：https://github.com/jzubizarreta/dsm
DVOSLAM： Dense visual SLAM for RGB-D camerasPaper：http://vision.informatik.tu-muenchen.de/_media/spezial/bib/kerl13iros.pdf
Code1：https://github.com/tum-vision/dvo_slam
Code2：https://github.com/tum-vision/dvo
Stereo odometry based on careful feature selection and trackingPaper：https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7324219
Code：https://github.com/Mayankm96/Stereo-Odometry-SOFT
OKVIS: Open Keyframe-based Visual-Inertial SLAMCode：https://github.com/gaoxiang12/okvis
Trifo-VIO: Robust and Efficient Stereo Visual Inertial Odometry using Points and LinesPaper：https://arxiv.org/pdf/1803.02403.pdf
Code：https://github.com/UMiNS/Trifocal-tensor-VIO
PL-VIO: Tightly-Coupled Monocular Visual–Inertial Odometry Using Point and Line FeaturesPaper：https://www.mdpi.com/1424-8220/18/4/1159/html
Overview of visual inertial navigation A Review of Visual-Inertial Simultaneous Localization and Mapping from Filtering-Based and Optimization-Based Perspectives：https://ieeexplore.ieee.org/document/5423178
https://www.mdpi.com/2218-6581/7/3/45
VINet: Visual-Inertial Odometry as a Sequence-to-Sequence Learning ProblemPaper：https://arxiv.org/abs/1701.08376
Code：https://github.com/HTLife/VINet
DeepVO: Towards End-to-End Visual Odometry with Deep Recurrent Convolutional Neural NetworksCode：https://github.com/ildoonet/deepvo
https://github.com/sladebot/deepvo
https://github.com/themightyoarfish/deepVO
https://github.com/fshamshirdar/DeepVO (pytorch)
Paper：http://www.cs.ox.ac.uk/files/9026/DeepVO.pdf
UnDeepVO: Implementation of Monocular Visual Odometry through Unsupervised Deep LearningCode：https://github.com/drmaj/UnDeepVO
Paper：https://arxiv.org/pdf/1709.06841.pdf
SfM-Net: SfM-Net: Learning of Structure and Motion from VideoCode： https://github.com/waxz/sfm_net
Paper： https://arxiv.org/pdf/1704.07804v1.pdf
CNN-SLAM: CNN-SLAM: Real-time dense monocular SLAM with learned depth predictionCode： https://github.com/iitmcvg/CNN_SLAM
Paper：https://arxiv.org/pdf/1704.03489.pdf
PoseNet: Posenet: A convolutional network for real-time 6-dof camera relocalization(ICCV2015)Code：https://github.com/alexgkendall/caffe-posenet or https://github.com/kentsommer/tensorflow-posenet
Paper：https://arxiv.org/pdf/1505.07427.pdf or https://arxiv.org/pdf/1509.05909.pdf
VidLoc: VidLoc: 6-doF video-clip relocalizationCode: https://github.com/futurely/deep-camera-relocalization
Paper: https://arxiv.org/pdf/1702.06521.pdf
NetVLAD: NetVLAD: CNN architecture for weakly supervised place recognition(CVPR2016)Code: https://github.com/Relja/netvlad (Matlab) or https://github.com/lyakaap/NetVLAD-pytorch
Paper: https://arxiv.org/pdf/1511.07247.pdf
DeMoN: Depth and Motion Network for Learning Monocular Stereo（CVPR2017）Code: https://github.com/lmb-freiburg/demon
Paper: https://arxiv.org/pdf/1612.02401v2.pdf
Learned Stereo MachineCode: https://github.com/akar43/lsm
Paper: https://arxiv.org/pdf/1708.05375.pdf
SfMLearner: Unsupervised Learning of Depth and Ego-Motion from VideoCode: https://github.com/tinghuiz/SfMLearner
Paper: https://arxiv.org/pdf/1704.07813.pdf
Toward Geometric Deep SLAMCode: UNopen(https://github.com/mtrasobaresb)
Paper: https://arxiv.org/pdf/1707.07410v1.pdf
Neural SLAM : Learning to Explore with External MemoryCode: UNopen
Paper: https://arxiv.org/pdf/1706.09520.pdf
PoseConvGRU: A Monocular Approach for Visual Ego-motion Estimation by Learning(2019)Code: UNopen
Paper: https://arxiv.org/pdf/1906.08095.pdf
Semi-Dense 3D Semantic Mapping from Monocular SLAM(2016)Code: UNopen
Paper: https://arxiv.org/pdf/1611.04144.pdf
Image-based 3D Object Reconstruction: State-of-the-Art and Trends in the Deep Learning Era(2019)Paper: https://arxiv.org/pdf/1906.06543.pdf
DeepMVS: DeepMVS: Learning Multi-view Stereopsis(CVPR2018)Code: https://github.com/phuang17/DeepMVS
Paper: https://phuang17.github.io/DeepMVS/index.html
Paper: https://arxiv.org/pdf/1804.00650.pdf
MVSNet: Mvsnet: Depth inference for unstructured multi-view stereo(ECCV2018)Code1: https://github.com/YoYo000/MVSNet
Code2: https://github.com/YoYo000/BlendedMVS
Paper: https://arxiv.org/pdf/1804.02505.pdf
PointMVSNet:Point-based Multi-view Stereo NetworkCode: https://github.com/callmeray/PointMVSNet
Paper: https://arxiv.org/pdf/1908.04422.pdf
Recurrent MVSNet: Recurrent MVSNet for High-resolution Multi-view Stereo Depth Inference(CVPR2019)Code: https://github.com/YoYo000/MVSNet
Paper: https://arxiv.org/pdf/1902.10556.pdf
(ESP-VO) End-to-End, Sequence-to-Sequence Probabilistic Visual Odometry through Deep Neural NetworksCode: https://github.com/espnet/espnet
https://www.seas.upenn.edu/~meam620/slides/kinematicsI.pdf Lidar Visual odometry Lidar-Monocular Visual OdometryCode：https://github.com/johannes-graeter/limo
Paper：https://arxiv.org/pdf/1807.07524.pdf
RGBD and LIDAR Google’s cartographer. Available on ROS CAE-LO: LiDAR Odometry Leveraging Fully Unsupervised Convolutional Auto-Encoder for Interest Point Detection and Feature DescriptionPaper：https://arxiv.org/ftp/arxiv/papers/2001/2001.01354.pdf
Code：https://github.com/SRainGit/CAE-LO
Other open source projectsDynaSLAM A SLAM system robust in dynamic environments for monocular, stereo and RGB-D setups
openvslam A Versatile Visual SLAM Framework
cartographerCode：https://github.com/googlecartographer/cartographer
Paper：https://google-cartographer.readthedocs.io/en/latest/
A-LOAM（Advanced implementation of LOAM）LOAM: Lidar Odometry and Mapping in Real-time
Code1：https://github.com/HKUST-Aerial-Robotics/A-LOAM
Code2：https://github.com/cuitaixiang/LOAM_NOTED
Paper：http://roboticsproceedings.org/rss10/p07.pdf
SemanticFusion: Dense 3D semantic mapping with convolutional neural networksCode: https://github.com/seaun163/semanticfusion
Paper: https://arxiv.org/pdf/1609.05130v2.pdf
ORB_SLAM2_SSD_SemanticCode：https://github.com/Ewenwan/ORB_SLAM2_SSD_Semantic
Visual Semantic SLAM with Landmarks for Large-Scale Outdoor EnvironmentPaper：https://arxiv.org/pdf/2001.01028.pdf
Code：https://github.com/1989Ryan/Semantic_SLAM/
Datasets TUM Universtiy KITTI Vision benchmark UNI-Freiburg ADVIO Oxford RobotCar Dataset HRI (Honda Research Institute) Driving Datasets Argoverse nuScenes Waymo Open Dataset Lyft Level 5 AV Dataset 2019 KAIST Urban Dataset Libraries Basic vision and trasformation libraries OpenCV Eigen Sophus ROS PointCloud Thread-safe queue libraries concurrentqueue Intel® TBB Facebook folly PC Loop detection dorian3d Graph Optimization ceres-solver g2o gtasm Vertigo Map library ETHZ ASL/Grip Map OmniMapper OctoMap Tools rgbd-dataset tool from TUM evo - evaluation tool for different trajectory formats作者：冲动的MJ
