SLAM is an abbreviation for "Simultaneous localization and mapping".
SLAM is a field with high entry barriers for beginners.
As a beginner learning SLAM, I created this repository to organize resources that can be used as a reference when learning SLAM for the first time.
I made this repository based on the content from the SLAM KR community and the activities of my github followers!
If you are Korean, you will prefer to look korean.md.
- Mathematics
- Roadmap
- Review Paper & Survey Paper
- Lecture
- Books
- Awesome-list
- Recommended github repository
- Some Math Basics often used in Photogrammetry (Cyrill Stachniss, 2021) [Youtube]
An brief, informal collection of math basics and tools that are often used in Photogrammetry (SVD, Least Squares with Gauss Newton)
- Linear Algebra Primer - Stanford Vision Lab [pdf]
Lecture slide for Linear Algebra Review
Roadmap to becoming a Visual-SLAM developer
- "SLAM tutorial : Part 1" By H. Durrant-Whyte and T. Bailey (IEEE Robotics & Automation Magazine 2006) - [pdf]
- "SLAM tutorial : Part 2" By H. Durrant-Whyte and T. Bailey (IEEE Robotics & Automation Magazine 2006) - [pdf]
- "Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age" By C. Cadena et al. (IROS 2016) - [pdf]
- "Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving" By G. Bresson, Z. Alsayed et al. (IEEE Transactions on Intelligent Vehicles 2017) - [pdf]
- "Comparison of modern open-source visual SLAM approaches" By Dinar Sharafutdinov et al. (ArXiv 2021) - [pdf]
- "Visual Odometry Part I: The First 30 Years and Fundamentals" By Davide Scaramuzza and Friedrich Fraundorfer - [pdf]
- "Visual Odometry Part II: Matching, Robustness, Optimization, and Applications" By Davide Scaramuzza and Friedrich Fraundorfer - [pdf]
- "A Comparison of Modern General-Purpose Visual SLAM Approaches" By Alexey Merzlyakov et al. (IROS 2021) - [pdf]
- "Visual-Inertial Navigation: A Concise Review" By Guoquan Huang (ICRA 2019) - [pdf]
- "Run Your Visual-Inertial Odometry on NVIDIA Jetson: Benchmark Tests on a Micro Aerial Vehicle" By Jinwoo Jeon et al. (ArXiv 2021) - [pdf]
- "A Survey on Deep Learning for Localization and Mapping: Towards the Age of Spatial Machine Intelligence" By Changhao Chen et al. - [pdf]
- "A Survey on Global LiDAR Localization: Challenges, Advances and Open Problems" By Huan Yin et al. - [pdf]
- Mobile Robotics course for the Winter 2020 Semester at the University of Michigan - [Webpage]
- Mobile Sensing And Robotics 2 By Stachniss (2021) - [Webpage]
- AirLab Summer School 2020 in Carnegie Mellon university - [Webpage]
- Tartan SLAM Series in AirLab - [Webpage]
- Tartan SLAM Series Fall Edition in AirLab - [Webpage]
- Probabilistic Robotics By Sebastian Thrun, Wolfram Burgard and Dieter Fox - [Webpage], [pdf]
- Computer Vision: Algorithms and Applications, 2nd ed. By Richard Szeliski - [Webpage]
- STATE ESTIMATION FOR ROBOTICS By Timothy D. Barfoot - [pdf]
- Awesome-SLAM
- awesome-visual-slam
- Awesome LIDAR
- Awesome SLAM Datasets
- awesome-photogrammetry
- Awesome Robotic Tooling
- Awesome Robot Operating System 2 (ROS 2)
- awesome-modern-cpp : Modern C++ is important language to learn SLAM system.
This repository tracks advancement of SLAM system. (2021 ver)
The English version of 14 lectures on visual SLAM. You could see source code in Slambook2.
pySLAM contains a monocular Visual Odometry (VO) pipeline in Python.
An OpenCV based implementation of Monocular Visual Odometry
This codebase proposes modular light python and pytorch implementations of several LiDAR Odometry methods.
Full-python LiDAR SLAM using ICP and Scan Context
Python sample codes for robotics algorithms.
Kalman Filter book using Jupyter Notebook.
An Invitation to 3D Vision: A Tutorial for Everyone