└── README.md /README.md: -------------------------------------------------------------------------------- 1 | 2 | 3 | # SLAM 4 | 5 | ## 优秀开源项目汇总 6 | 7 | [https://github.com/OpenSLAM/awesome-SLAM-list](https://github.com/OpenSLAM/awesome-SLAM-list) 8 | 9 | [https://github.com/tzutalin/awesome-visual-slam](https://github.com/tzutalin/awesome-visual-slam) 10 | 11 | https://github.com/kanster/awesome-slam 12 | 13 | https://github.com/YoujieXia/Awesome-SLAM 14 | 15 | [Recent_SLAM_Research](https://github.com/YiChenCityU/Recent_SLAM_Research) 16 | 17 | [https://github.com/youngguncho/awesome-slam-datasets](https://github.com/youngguncho/awesome-slam-datasets) 18 | 19 | [https://github.com/marknabil/SFM-Visual-SLAM](https://github.com/marknabil/SFM-Visual-SLAM) 20 | 21 | [https://github.com/ckddls1321/SLAM_Resources](https://github.com/ckddls1321/SLAM_Resources) 22 | 23 | ## 激光SLAM 24 | 25 | > 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。 26 | > 27 | > 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。 28 | > 29 | > 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。 30 | 31 | ### 数据预处理 32 | 33 | ### 点云匹配 34 | 35 | ### 地图构建 36 | 37 | ## 视觉SLAM 38 | 39 | ### Books 40 | 41 | - [视觉SLAM十四讲]() 高翔 42 | - [机器人学中的状态估计]() 43 | - [概率机器人]() 44 | - [Simultaneous Localization and Mapping for Mobile Robots: Introduction and Methods](http://www.igi-global.com/book/simultaneous-localization-mapping-mobile-robots/66380) by Juan-Antonio Fernández-Madrigal and José Luis Blanco Claraco, 2012 45 | - [Simultaneous Localization and Mapping: Exactly Sparse Information Filters ](http://www.worldscientific.com/worldscibooks/10.1142/8145/)by Zhan Wang, Shoudong Huang and Gamini Dissanayake, 2011 46 | - [An Invitation to 3-D Vision -- from Images to Geometric Models](http://vision.ucla.edu/MASKS/) by Yi Ma, Stefano Soatto, Jana Kosecka and Shankar S. Sastry, 2005 47 | - [Multiple View Geometry in Computer Vision](http://www.robots.ox.ac.uk/~vgg/hzbook/) by Richard Hartley and Andrew Zisserman, 2004 48 | - [Numerical Optimization](http://home.agh.edu.pl/~pba/pdfdoc/Numerical_Optimization.pdf) by Jorge Nocedal and Stephen J. Wright, 1999 49 | 50 | ### Courses&&Lectures 51 | 52 | - [SLAM Tutorial@ICRA 2016](http://www.dis.uniroma1.it/~labrococo/tutorial_icra_2016/) 53 | - [Geometry and Beyond - Representations, Physics, and Scene Understanding for Robotics](http://rss16-representations.mit.edu/) at Robotics: Science and Systems (2016) 54 | - [Robotics - UPenn](https://www.coursera.org/specializations/robotics) on Coursera by Vijay Kumar (2016) 55 | - [Robot Mapping - UniFreiburg](http://ais.informatik.uni-freiburg.de/teaching/ws15/mapping/) by Gian Diego Tipaldi and Wolfram Burgard (2015-2016) 56 | - [Robot Mapping - UniBonn](http://www.ipb.uni-bonn.de/robot-mapping/) by Cyrill Stachniss (2016) 57 | - [Introduction to Mobile Robotics - UniFreiburg](http://ais.informatik.uni-freiburg.de/teaching/ss16/robotics/) by Wolfram Burgard, Michael Ruhnke and Bastian Steder (2015-2016) 58 | - [Computer Vision II: Multiple View Geometry - TUM](http://vision.in.tum.de/teaching/ss2016/mvg2016) by Daniel Cremers ( Spring 2016) 59 | - [Advanced Robotics - UCBerkeley](http://www.cs.berkeley.edu/~pabbeel/) by Pieter Abbeel (Fall 2015) 60 | - [Mapping, Localization, and Self-Driving Vehicles](https://www.youtube.com/watch?v=x5CZmlaMNCs) at CMU RI seminar by John Leonard (2015) 61 | - [The Problem of Mobile Sensors: Setting future goals and indicators of progress for SLAM](http://ylatif.github.io/movingsensors/) sponsored by Australian Centre for Robotics and Vision (2015) 62 | - [Robotics - UPenn](https://alliance.seas.upenn.edu/~meam620/wiki/index.php?n=Main.HomePage) by Philip Dames and Kostas Daniilidis (2014) 63 | - [Autonomous Navigation for Flying Robots](http://vision.in.tum.de/teaching/ss2014/autonavx) on EdX by Jurgen Sturm and Daniel Cremers (2014) 64 | - [Robust and Efficient Real-time Mapping for Autonomous Robots](https://www.youtube.com/watch?v=_W3Ua1Yg2fk) at CMU RI seminar by Michael Kaess (2014) 65 | - [KinectFusion - Real-time 3D Reconstruction and Interaction Using a Moving Depth Camera](https://www.youtube.com/watch?v=bRgEdqDiOuQ) by David Kim (2012) 66 | 67 | ### Code 68 | 69 | 1. [ORB-SLAM](https://github.com/raulmur/ORB_SLAM) 70 | 2. [LSD-SLAM](https://github.com/tum-vision/lsd_slam) 71 | 3. [ORB-SLAM2](https://github.com/raulmur/ORB_SLAM2) 72 | 4. [DVO: Dense Visual Odometry](https://github.com/tum-vision/dvo_slam) 73 | 5. [SVO: Semi-Direct Monocular Visual Odometry](https://github.com/uzh-rpg/rpg_svo) 74 | 6. [G2O: General Graph Optimization](https://github.com/RainerKuemmerle/g2o) 75 | 7. [RGBD-SLAM](https://github.com/felixendres/rgbdslam_v2) 76 | 77 | | Project | Language | License | 78 | | ------------------------------------------------------------ | -------- | -------------------------- | 79 | | [COSLAM](http://drone.sjtu.edu.cn/dpzou/project/coslam.php) | C++ | GNU General Public License | 80 | | [DSO-Direct Sparse Odometry](https://github.com/JakobEngel/dso) | C++ | GPLv3 | 81 | | [DTSLAM-Deferred Triangulation SLAM](https://github.com/plumonito/dtslam) | C++ | modified BSD | 82 | | [LSD-SLAM](https://github.com/tum-vision/lsd_slam/) | C++/ROS | GNU General Public License | 83 | | [MAPLAB-ROVIOLI](https://github.com/ethz-asl/maplab) | C++/ROS | Apachev2.0 | 84 | | [OKVIS: Open Keyframe-based Visual-Inertial SLAM](https://github.com/ethz-asl/okvis) | C++ | BSD | 85 | | [ORB-SLAM](https://github.com/raulmur/ORB_SLAM2) | C++ | GPLv3 | 86 | | [REBVO - Realtime Edge Based Visual Odometry for a Monocular Camera](https://github.com/JuanTarrio/rebvo) | C++ | GNU General Public License | 87 | | [SVO semi-direct Visual Odometry](https://github.com/uzh-rpg/rpg_svo) | C++/ROS | GNU General Public License | 88 | --------------------------------------------------------------------------------