视觉里程计综述
- 引言
- Visual Odometry or VSLAM
- OF-VO:Robust and Efficient Stereo Visual Odometry Using Points and Feature Optical Flow
- SLAMBook
- SVO: Fast Semi-Direct Monocular Visual Odometry
- Robust Odometry Estimation for RGB-D Cameras
- Parallel Tracking and Mapping for Small AR Workspaces
- ORBSLAM
- A ROS Implementation of the Mono-Slam Algorithm
- DTAM: Dense tracking and mapping in real-time
- LSD-SLAM: Large-Scale Direct Monocular SLAM
- RGBD-Odometry (Visual Odometry based RGB-D images)
- Py-MVO: Monocular Visual Odometry using Python
- Stereo-Odometry-SOFT
- monoVO-python
- DVO:Robust Odometry Estimation for RGB-D Cameras
- Dense Visual Odometry and SLAM (dvo_slam)
- REVO:Robust Edge-based Visual Odometry
- xivo
- PaoPaoRobot
- ygz-slam
- RTAB MAP
- MYNT-EYE
- Kintinuous
- ElasticFusion
- Co-Fusion:Real-time Segmentation, Tracking and Fusion of Multiple Objects
- Visual Inertial Odometry or VIO-SLAM
- R-VIO:Robocentric Visual-Inertial Odometry
- Kimera-VIO: Open-Source Visual Inertial Odometry
- ADVIO: An Authentic Dataset for Visual-Inertial Odometry
- MSCKF_VIO:Robust Stereo Visual Inertial Odometry for Fast Autonomous Flight
- LIBVISO2: C++ Library for Visual Odometry 2
- Stereo Visual SLAM for Mobile Robots Navigation
- Combining Edge Images and Depth Maps for Robust Visual Odometry
- HKUST Aerial Robotics Group
- VINS-Fusion:Online Temporal Calibration for Monocular Visual-Inertial Systems
- Monocular Visual-Inertial State Estimation for Mobile Augmented Reality
- Computer Vision Group TUM Department of Informatics Technical University of Munich
- Visual-Inertial DSOhttps://vision.in.tum.de/research/vslam/vi-dso
- Stereo odometry based on careful feature selection and tracking
- OKVIS: Open Keyframe-based Visual-Inertial SLAM
- Trifo-VIO: Robust and Efficient Stereo Visual Inertial Odometry using Points and Lines
- PL-VIO: Tightly-Coupled Monocular Visual–Inertial Odometry Using Point and Line Features
- Overview of visual inertial navigation
- Based CNN(Net VO or Net VSLAM)
- VINet: Visual-Inertial Odometry as a Sequence-to-Sequence Learning Problem
- DeepVO: Towards End-to-End Visual Odometry with Deep Recurrent Convolutional Neural Networks
- UnDeepVO - Implementation of Monocular Visual Odometry through Unsupervised Deep Learning
- (ESP-VO) End-to-End, Sequence-to-Sequence Probabilistic Visual Odometry through Deep Neural Networks
- Lidar Visual odometry
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github:https://github.com/MichaelBeechan
CSDN:https://blog.csdn.net/u011344545
欢迎star/fork:https://github.com/MichaelBeechan/Visual-Odometry-Review
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这是一篇关于目前开源SLAM、开源VO视觉里程计的综述博客
引言
SLAM is mainly divided into two parts: the front end and the back end. The front end is the visual odometery(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.
Visual Odometry or VSLAM
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
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
Code1:https://github.com/raulmur/ORB_SLAM2
Code2:https://github.com/raulmur/ORB_SLAM
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
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
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. 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
Visual Inertial Odometry or VIO-SLAM
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
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
Based CNN(Net VO or Net VSLAM)
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:UnDeepVO - Implementation of Monocular Visual Odometry through Unsupervised Deep Learning
(ESP-VO) End-to-End, Sequence-to-Sequence Probabilistic Visual Odometry through Deep Neural Networks
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. Available on ROS
Other open source projects
DynaSLAM A SLAM system robust in dynamic environments for monocular, stereo and RGB-D setups
openvslam A Versatile Visual SLAM Framework