Awesome
持续更新修改中
2016.12.20
awesome-SLAM-list
=========================================== ##Contents
- Tutorials-SLAM
- [SLAM Tutorial & Survey](#SLAM Tutorial & Survey)
- [Computer Vision Books](#Computer Vision Books)
- [Video & Courses](#Video & Courses)
- Papers-SLAM
- [Visual Odometry](#Visual Odometry)
- ORB-SLAM
- Mono-SLAM
- LSD-SLAM
- RGBD-SLAM
- ElasticFusion
- Others-SLAM
- OpenSource-SLAM
- Feature Detection & Description
- [Feature Papers](#Feature Papers)
- [Feature Libs](#Feature Libs)
- Datasets
- License
- Contributing
- Others
<a name="Tutorials-SLAM"></a>
Tutorials-SLAM
<a name="SLAM Tutorial & Survey"></a>
SLAM Tutorial & Survey
OpenSLAM The OpenSLAM Team: Cyrill Stachniss, Udo Frese, Giorgio Grisetti
ICRA 2016 Aerial Robotics - (Visual odometry) D. Scaramuzza
Simultaneous Localization And Mapping: Present, Future, and the Robust-Perception Age. C. Cadena, L. Carlone, H. Carrillo, Y. Latif, D. Scaramuzza, J. Neira, I. D. Reid, J. J. Leonard. "The paper summarizes the outcome of the workshop “The Problem of Mobile Sensors: Setting future goals and indicators of progress for SLAM” held during the Robotics: Science and System (RSS) conference (Rome, July 2015)."
Visual Odometry: Part I - The First 30 Years and Fundamentals, D. Scaramuzza and F. Fraundorfer, IEEE Robotics and Automation Magazine, Volume 18, issue 4, 2011
Visual Odometry: Part II - Matching, robustness, optimization, and applications, F. Fraundorfer and D. Scaramuzza, IEEE Robotics and Automation Magazine, Volume 19, issue 2, 2012
<a name="Computer Vision Books"></a>
Computer Vision Books
Multiple View Geometry in Computer Vision Second Edition. Richard Hartley & Andrew Zisserman. 2004.
Computer Vision: Algorithms and Applications. R. Szeliski. 2010.
Development of Scientific Applications with the Mobile Robot Programming Toolkit (MRPT)
<a name="Video"></a>
Video & Courses & Blogs
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Courses: Robotics Lecture Course (course code 333) | Author: Andrew Davison https://www.doc.ic.ac.uk/~ajd/Robotics/index.html
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Courses: Robotics | Author: The University of Pennsylvania
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Robotics:Aerial Robotics
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Robotics:Computational Motion Planning
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Robotics:Mobility
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Robotics:Perception
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Robotics:Estimation and Learning
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Robotics:Capstone
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Courses: 视觉SLAM十四讲 | Author: 高翔
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Blogs: 视觉SLAM | Author: 高翔 | Email:gaoxiang12@mails.tsinghua.edu.cn
<a name="Papers-SLAM"></a> #Papers-SLAM
<a name="Visual Odometry"></a>
Visual Odometry (image based only)
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Real-time simultaneous localisation and mapping with a single camera. A. J. Davison. ICCV 2003.
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Visual odometry. D. Nister, O. Naroditsky, and J. Bergen. CVPR 2004.
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Real time localization and 3d reconstruction. E. Mouragnon, M. Lhuillier, M. Dhome, F. Dekeyser, and P. Sayd. CVPR 2006.
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Parallel Tracking and Mapping for Small AR Workspaces. G. Klein, D. Murray. ISMAR 2007.
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Real-Time 6-DOF Monocular Visual SLAM in a Large-scale Environments. H. Lim, J. Lim, H. Jin Kim. ICRA 2014.
<a name="ORB-SLAM"></a>
ORB-SLAM
- ORB-SLAM: a Versatile and Accurate Monocular SLAM System
- ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras
<a name="Mono-SLAM"></a>
Mono-SLAM
- MonoSLAM: Real-Time Single Camera SLAM
- Inverse Depth Parametrization for Monocular SLAM
<a name="LSD-SLAM"></a>
LSD-SLAM
- LSD-SLAM: Large-Scale Direct Monocular SLAM
- Large-Scale Direct SLAM for Omnidirectional Cameras
- Large-Scale Direct SLAM with Stereo Cameras
- Reconstructing Street-Scenes in Real-Time From a Driving Car
<a name="RGBD-SLAM"></a>
RGBD-SLAM
- 3-D Mapping With an RGB-D Camera
- An Evaluation of the RGB-D SLAM System
- A Benchmark for the Evaluation of RGB-D SLAM Systems
- Real-time dense appearance-based SLAM for RGB-D sensors
- RGB-D Mapping: Using Depth Cameras for Dense 3D Modeling of Indoor Environments
- RGB-D mapping: Using Kinect-style depth cameras for dense 3D modeling of indoor environments
<a name="ElasticFusion"></a>
ElasticFusion
- ElasticFusion: Dense SLAM Without A Pose Graph
- ElasticFusion: Real-Time Dense SLAM and Light Source Estimation
<a name="Others-SLAM"></a>
Others-SLAM
- DTAM: Dense Tracking and Mapping in Real-Time
- Dense Visual SLAM for RGB-D Cameras
- KinectFusion: Real-Time Dense Surface Mapping and Tracking ∗
- Parallel Tracking and Mapping for Small AR Workspaces
- SVO: Fast Semi-Direct Monocular Visual Odometry
<a name="OpenSource-SLAM"></a>
OpenSource-SLAM
<a name="OpenSourceSLAM"></a>
OpenSource-SLAM
<a name="SLAM-Migration"></a>
SLAM-Migration
IOS
Project | Platform | Link | Language | License | Video |
---|---|---|---|---|---|
ygx2011/ORB_SLAM-IOS | iOS | https://github.com/ygx2011/ORB_SLAM-IOS | C++/Objective-C/Unity3D | Plane-SLAM-AR-ios | |
egoist-sx/ORB_SLAM_iOS | iOS | https://github.com/egoist-sx/ORB_SLAM_iOS | C++/Objective-C |
Android
Project | Platform | Link | Language | License | Video |
---|---|---|---|---|---|
FangGet/ORB_SLAM2_Android | Android | https://github.com/FangGet/ORB_SLAM2_Android | C++/java |
ZED
Project | Platform | Link | Language | License | Video |
---|---|---|---|---|---|
ygx2011/Stereo_SLAM_AR | OSX/ZED | https://github.com/ygx2011/Stereo_SLAM_AR | C++/Objective-C | ||
ygx2011/ZED_Stereo_ORBSLAM | OSX/ZED | https://github.com/ygx2011/ZED_Stereo_ORBSLAM | C++/Objective-C |
<a name="OpenSourceMinimization"></a>
OpenSource Minimization
Project | Language | License |
---|---|---|
G2O | C++ | BSD License + L/GPL3 restriction |
Ceres Solver | C++ | BSD License |
GTSAM | C++ | BSD License |
NLopt | C++ | LGPL |
<a name="NearestNeighborSearch"></a>
Nearest Neighbor Search
Project | Language | License |
---|---|---|
ANN | C++ | GNU General Public License |
Annoy | C++ | Apache License |
FLANN | C++ | BSD License |
Nanoflann | C++ | BSD License |
<a name="UsefulLib"></a>
Useful Lib
TODO:....
- Eigen
- ...
<a name="Features"></a>
Feature Detection & Description
<a name="Feature Papers"></a>
Feature Papers
Summarize: A survey of recent advances in visual feature detection Yali Li a,b, Shengjin Wang a,b,n, Qi Tian c, Xiaoqing Ding a,b
<table> <tr> <th width="200px"> Category </th> <th width="150px"> Classification </th> <th width="150px"> Methods </th> <th> Papers </th> </tr> <tr> <td rowspan="17">Blob Detection</td> <td rowspan="14">PDE Based</td> <td> LoG </td> <td> </td> </tr> <tr> <td> DoG </td> <td> </td> </tr> <tr> <td> DoH </td> <td> </td> </tr> <tr> <td> Hessian–Laplacian </td> <td> </td> </tr> <tr> <td> SIFT </td> <td> </td> </tr> <tr> <td> SURF </td> <td> </td> </tr> <tr> <td> Cer-SURF </td> <td> </td> </tr> <tr> <td> DART </td> <td> </td> </tr> <tr> <td> Rank-SIFT </td> <td> </td> </tr> <tr> <td> RLOG </td> <td> </td> </tr> <tr> <td> MO-GP </td> <td> </td> </tr> <tr> <td> KAZE </td> <td> </td> </tr> <tr> <td> A-KAZE </td> <td> </td> </tr> <tr> <td> WADE </td> <td> </td> </tr> <tr> <td rowspan="3">Template Based</td> <td> ORB </td> <td> </td> </tr> <tr> <td> BRISK </td> <td> </td> </tr> <tr> <td> FREAK </td> <td> </td> </tr> </table><a name="Feature Libs"></a>
Feature Libs
TODO:....
Project | Detection | Description |
---|---|---|
AKAZE | x | MSURF/MLDB |
DART | x | x |
KAZE | x | MSURF/MLDB |
LIOP/MIOP | x | |
LIFT (machine learning) | x | x |
MROGH | x | |
SIFT | x | x |
SURF | x | x |
SFOP | x | |
... |
"Real time" oriented methods
Project | Detection | Description |
---|---|---|
BRIEF | x | |
BRISK | x | x |
FAST | x | |
FREAK | x | |
FRIF | x | x |
HIPS | x | |
LATCH | x | |
MOPS | x | |
PhonySift | Multi-scale Fast | Reduced Sift grid |
ORB | Multiscale Fast | Oriented BRIEF |
<a name="Datasets"></a>
Datasets
- Malaga Dataset
- Tum: Computer Vision Lab: RGB-D
- KITTI Dataset
- University of Freiburg: Department of Computer Science
- MRPT
- ICDL-NUIM
<a name="License"></a>
License
<a name="Contributing"></a>
Contributing
- 泡泡机器人SLAM: 微信公众号:paopaorobot_slam
- 高翔博士:https://github.com/gaoxiang12
- 应高选:https://github.com/ygx2011
- awesome_3DReconstruction_list
- Markdown——入门指南
- awesome-computer-vision
<a name="Others"></a>