# Visual Odometry（VO）-SLAM-Review SLAM 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 ======================================================================= github：https://github.com/MichaelBeechan CSDN：https://blog.csdn.net/u011344545 ======================================================================= 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 Flow Code：https://github.com/MichaelBeechan/MyStereoLibviso2 ## SLAMBook Paper：14 Lectures on Visual SLAM: From Theory to Practice, Code：https://github.com/gaoxiang12/slambook ## SLAMBook2 Code：https://github.com/gaoxiang12/slambook2 ## SVO: Fast Semi-Direct Monocular Visual Odometry Paper：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 Images Paper：http://www.cs.nuim.ie/research/vision/data/icra2013/Whelan13icra.pdf Code：https://github.com/tum-vision/dvo ## Parallel Tracking and Mapping for Small AR Workspaces Paper：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 ## ORBSLAM Code3：https://github.com/MichaelBeechan/ORBSLAM3 Paper3：ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM：https://arxiv.org/pdf/2007.11898.pdf Code2：https://github.com/raulmur/ORB_SLAM2 Paper2：ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras：https://arxiv.org/pdf/1610.06475.pdf Code1：https://github.com/raulmur/ORB_SLAM Paper1:ORB-SLAM: A Versatile and Accurate Monocular SLAM System:https://arxiv.org/pdf/1502.00956.pdf ## A ROS Implementation of the Mono-Slam Algorithm Paper：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-time Paper：https://ieeexplore.ieee.org/document/6126513 Code：https://github.com/anuranbaka/OpenDTAM ## LSD-SLAM: Large-Scale Direct Monocular SLAM Paper：http://pdfs.semanticscholar.org/c13c/b6dfd26a1b545d50d05b52c99eb87b1c82b2.pdf https://vision.in.tum.de/research/vslam/lsdslam Code：https://github.com/tum-vision/lsd_slam ## RGBD-Odometry (Visual Odometry based RGB-D images) ## Real-Time Visual Odometry from Dense RGB-D Images Code：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 Python Code：https://github.com/Transportation-Inspection/visual_odometry Video：https://www.youtube.com/watch?v=E8JK19TmTL4&feature=youtu.be ## Stereo-Odometry-SOFT MATLAB 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 Latency Paper：https://arxiv.org/pdf/2001.00714.pdf Code：https://github.com/ivalab/GF_ORB_SLAM ## monoVO-python Code1：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 Cameras Code：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 Edges Code：https://github.com/fabianschenk/REVO Paper：https://graz.pure.elsevier.com/ ## xivo X Inertial-aided Visual Odometry Code：https://github.com/ucla-vision/xivo Paper：[XIVO: X Inertial-aided Visual Odometry and Sparse Mapping](http://vision.ucla.edu/papers/tsotsosCS15.pdf) ## PaoPaoRobot Code：https://github.com/PaoPaoRobot ## ygz-slam Code：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](https://github.com/introlab/rtabmap). Available on ROS > Online 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 # MYNT-EYE 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 environment Code：https://github.com/mp3guy/Kintinuous # ElasticFusion ## ElasticFusion: Dense SLAM Without A Pose Graph ## ElasticFusion: Real-Time Dense SLAM and Light Source Estimation Paper：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 Objects Paper：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 Odometry Code：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 Odometry Code：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 Flight Paper：https://arxiv.org/abs/1712.00036 Code：https://github.com/KumarRobotics/msckf_vio ## Kimera-VIO: Open-Source Visual Inertial Odometry Kimera: 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 2 Paper：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 SLAM Paper：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 Estimator Paper：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 Systems Paper：https://arxiv.org/pdf/1808.00692.pdf Code：https://github.com/HKUST-Aerial-Robotics/VINS-Fusion ## Monocular Visual-Inertial State Estimation for Mobile Augmented Reality Paper：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 Odometry Code：https://github.com/JingeTu/StereoDSO ### Visual-Inertial DSOhttps://vision.in.tum.de/research/vslam/vi-dso ### DVSO：https://vision.in.tum.de/research/vslam/dvso ### DSO with Loop-closure and Sim(3) pose graph optimization：https://vision.in.tum.de/research/vslam/ldso ## Stereo odometry based on careful feature selection and tracking Paper：https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7324219 Code：https://github.com/Mayankm96/Stereo-Odometry-SOFT ## OKVIS: Open Keyframe-based Visual-Inertial SLAM Code：https://github.com/gaoxiang12/okvis ## Trifo-VIO: Robust and Efficient Stereo Visual Inertial Odometry using Points and Lines Paper：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 Features Paper：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 Problem Paper：https://arxiv.org/abs/1701.08376 Code：https://github.com/HTLife/VINet ## DeepVO: Towards End-to-End Visual Odometry with Deep Recurrent Convolutional Neural Networks Code：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 Learning Code：https://github.com/drmaj/UnDeepVO Paper：https://arxiv.org/pdf/1709.06841.pdf ## SfM-Net: SfM-Net: Learning of Structure and Motion from Video Code： 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 prediction Code： 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 relocalization Code: 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 Machine Code: https://github.com/akar43/lsm Paper: https://arxiv.org/pdf/1708.05375.pdf ## SfMLearner: Unsupervised Learning of Depth and Ego-Motion from Video Code: https://github.com/tinghuiz/SfMLearner Paper: https://arxiv.org/pdf/1704.07813.pdf ## Toward Geometric Deep SLAM Code: UNopen(https://github.com/mtrasobaresb) Paper: https://arxiv.org/pdf/1707.07410v1.pdf ## Neural SLAM : Learning to Explore with External Memory Code: 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 Network Code: 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 Networks Code: https://github.com/espnet/espnet ## https://www.seas.upenn.edu/~meam620/slides/kinematicsI.pdf # Lidar Visual odometry ## Lidar-Monocular Visual Odometry Code：https://github.com/johannes-graeter/limo Paper：https://arxiv.org/pdf/1807.07524.pdf ## RGBD and LIDAR - [Google's cartographer](https://github.com/googlecartographer/cartographer). Available on ROS ## CAE-LO: LiDAR Odometry Leveraging Fully Unsupervised Convolutional Auto-Encoder for Interest Point Detection and Feature Description Paper：https://arxiv.org/ftp/arxiv/papers/2001/2001.01354.pdf Code：https://github.com/SRainGit/CAE-LO ## Other open source projects [DynaSLAM](https://github.com/BertaBescos/DynaSLAM) A SLAM system robust in dynamic environments for monocular, stereo and RGB-D setups [openvslam](https://github.com/xdspacelab/openvslam) A Versatile Visual SLAM Framework ## cartographer Code：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 networks Code: https://github.com/seaun163/semanticfusion Paper: https://arxiv.org/pdf/1609.05130v2.pdf ## ORB_SLAM2_SSD_Semantic Code：https://github.com/Ewenwan/ORB_SLAM2_SSD_Semantic ## Visual Semantic SLAM with Landmarks for Large-Scale Outdoor Environment Paper：https://arxiv.org/pdf/2001.01028.pdf Code：https://github.com/1989Ryan/Semantic_SLAM/ # Datasets - [TUM Universtiy](http://vision.in.tum.de/data/datasets/rgbd-dataset/download) - [KITTI Vision benchmark](http://www.cvlibs.net/datasets/kitti/eval_odometry.php) - [UNI-Freiburg](http://kaspar.informatik.uni-freiburg.de/~slamEvaluation/datasets.php) - [ADVIO](https://github.com/AaltoVision/ADVIO) - [Oxford RobotCar Dataset](https://robotcar-dataset.robots.ox.ac.uk/) - [HRI (Honda Research Institute) Driving Datasets](https://usa.honda-ri.com/honda-driving-datasets) - [Argoverse](https://www.argoverse.org/data.html) - [nuScenes](https://www.nuscenes.org) - [Waymo Open Dataset](https://waymo.com/open/) - [Lyft Level 5 AV Dataset 2019](https://level5.lyft.com/dataset/) - [KAIST Urban Dataset](https://irap.kaist.ac.kr/dataset/) # Libraries ## Basic vision and trasformation libraries - [OpenCV](http://opencv.org/) - [Eigen](http://eigen.tuxfamily.org/index.php?title=Main_Page) - [Sophus](https://github.com/strasdat/Sophus) - [ROS](http://www.ros.org/) - [PointCloud](http://pointclouds.org/) ## Thread-safe queue libraries - [concurrentqueue](https://github.com/cameron314/concurrentqueue) - [Intel® TBB](https://www.threadingbuildingblocks.org/) - [Facebook folly PC](https://github.com/facebook/folly/blob/master/folly/ProducerConsumerQueue.h) ## Loop detection - [dorian3d](https://github.com/dorian3d) ## Graph Optimization - [ceres-solver](https://github.com/ceres-solver/ceres-solver) - [g2o](https://github.com/RainerKuemmerle/g2o) - [gtasm](https://collab.cc.gatech.edu/borg/gtsam?destination=node%2F299) - [Vertigo](http://openslam.org/vertigo.html) ## Map library - [ETHZ ASL/Grip Map](https://github.com/ethz-asl/grid_map) - [OmniMapper](https://github.com/CognitiveRobotics/omnimapper/wiki) - [OctoMap](https://github.com/OctoMap/octomap) ## Tools - [rgbd-dataset tool from TUM](https://vision.in.tum.de/data/datasets/rgbd-dataset/tools) - [evo - evaluation tool for different trajectory formats](https://github.com/MichaelGrupp/evo)