https://github.com/OpenSLAM/awesome-SLAM-list
https://github.com/tzutalin/awesome-visual-slam
https://github.com/kanster/awesome-slam
https://github.com/YoujieXia/Awesome-SLAM
https://github.com/youngguncho/awesome-slam-datasets
https://github.com/marknabil/SFM-Visual-SLAM
https://github.com/ckddls1321/SLAM_Resources
分为前端和后端。其中前端主要完成匹配和位置估计，后端主要完成进一步的优化约束。
整个SLAM大概可以分为前端和后端，前端相当于VO（视觉里程计），研究帧与帧之间变换关系。首先提取每帧图像特征点，利用相邻帧图像，进行特征点匹配，然后利用RANSAC去除大噪声，然后进行匹配，得到一个pose信息（位置和姿态），同时可以利用IMU（Inertial measurement unit惯性测量单元）提供的姿态信息进行滤波融合。
后端则主要是对前端出结果进行优化，利用滤波理论（EKF、UKF、PF）、或者优化理论TORO、G2O进行树或者图的优化。最终得到最优的位姿估计。
- 视觉SLAM十四讲 高翔
- 机器人学中的状态估计
- 概率机器人
- Simultaneous Localization and Mapping for Mobile Robots: Introduction and Methods by Juan-Antonio Fernández-Madrigal and José Luis Blanco Claraco, 2012
- Simultaneous Localization and Mapping: Exactly Sparse Information Filters by Zhan Wang, Shoudong Huang and Gamini Dissanayake, 2011
- An Invitation to 3-D Vision -- from Images to Geometric Models by Yi Ma, Stefano Soatto, Jana Kosecka and Shankar S. Sastry, 2005
- Multiple View Geometry in Computer Vision by Richard Hartley and Andrew Zisserman, 2004
- Numerical Optimization by Jorge Nocedal and Stephen J. Wright, 1999
- SLAM Tutorial@ICRA 2016
- Geometry and Beyond - Representations, Physics, and Scene Understanding for Robotics at Robotics: Science and Systems (2016)
- Robotics - UPenn on Coursera by Vijay Kumar (2016)
- Robot Mapping - UniFreiburg by Gian Diego Tipaldi and Wolfram Burgard (2015-2016)
- Robot Mapping - UniBonn by Cyrill Stachniss (2016)
- Introduction to Mobile Robotics - UniFreiburg by Wolfram Burgard, Michael Ruhnke and Bastian Steder (2015-2016)
- Computer Vision II: Multiple View Geometry - TUM by Daniel Cremers ( Spring 2016)
- Advanced Robotics - UCBerkeley by Pieter Abbeel (Fall 2015)
- Mapping, Localization, and Self-Driving Vehicles at CMU RI seminar by John Leonard (2015)
- The Problem of Mobile Sensors: Setting future goals and indicators of progress for SLAM sponsored by Australian Centre for Robotics and Vision (2015)
- Robotics - UPenn by Philip Dames and Kostas Daniilidis (2014)
- Autonomous Navigation for Flying Robots on EdX by Jurgen Sturm and Daniel Cremers (2014)
- Robust and Efficient Real-time Mapping for Autonomous Robots at CMU RI seminar by Michael Kaess (2014)
- KinectFusion - Real-time 3D Reconstruction and Interaction Using a Moving Depth Camera by David Kim (2012)
- ORB-SLAM
- LSD-SLAM
- ORB-SLAM2
- DVO: Dense Visual Odometry
- SVO: Semi-Direct Monocular Visual Odometry
- G2O: General Graph Optimization
- RGBD-SLAM
| Project | Language | License |
|---|---|---|
| COSLAM | C++ | GNU General Public License |
| DSO-Direct Sparse Odometry | C++ | GPLv3 |
| DTSLAM-Deferred Triangulation SLAM | C++ | modified BSD |
| LSD-SLAM | C++/ROS | GNU General Public License |
| MAPLAB-ROVIOLI | C++/ROS | Apachev2.0 |
| OKVIS: Open Keyframe-based Visual-Inertial SLAM | C++ | BSD |
| ORB-SLAM | C++ | GPLv3 |
| REBVO - Realtime Edge Based Visual Odometry for a Monocular Camera | C++ | GNU General Public License |
| SVO semi-direct Visual Odometry | C++/ROS | GNU General Public License |