Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/SilenceOverflow/Awesome-SLAM

A curated list of SLAM resources
https://github.com/SilenceOverflow/Awesome-SLAM

List: Awesome-SLAM

slam vislam vslam

Last synced: about 2 months ago
JSON representation

A curated list of SLAM resources

Awesome Lists containing this project

README 

        
# Awesome-SLAM [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)

A curated list of SLAM resources



## Stay Tuned for Constant Updates



> Last updated: Mar. 14th, 2021. 



The repo is maintained by [Youjie Xia](https://github.com/YoujieXia). The repo mainly summarizes the awesome repositories relevant to SLAM/VO on GitHub, including those on the PC end, the mobile end and some learner-friendly tutorials. 



Regrading awesome SLAM papers, please refer to [**Awesome-SLAM-Papers**](https://github.com/YoujieXia/Awesome-SLAM-Papers).



If you want to know more about dependencies/packages of SLAM systems, please refer to [Installing Dependencies on Ubuntu 16.04 LTS towards SLAM Projects (Updating)](Misc/2018-03-10-Installing-Dependencies-on-Ubuntu-towards-SLAM-Projects.md).



If you think this repo is useful, please ***watch***, ***star*** or ***fork*** it!



Welcome to contribute to this repo, if you are interested in SLAM! Feel free to creat a pull request or contact me.



> Note: Name Format - `repository name: one-sentence introduction` (with link to the corresponding repo)



------



## Table of Contents



- [1. Hot SLAM Repos on GitHub](#HotRepos)

- [2. Visual SLAM](#VSLAM)

	- [2.1 Framework](#VFramework)

	- [2.2 Monocular](#VMono)

	- [2.3 Stereo](#VStereo)

	- [2.4 RGBD](#VRGBD)

	- [2.5 Others](#VOthers)

- [3. Visual Inertial SLAM](#VISLAM)

	- [3.1 Framework](#VIFramework)

	- [3.2 Monocular](#VIMono)

	- [3.3 Stereo](#VIStereo)

	- [3.4 RGBD](#VIRGBD)

	- [3.5 Others](#VIOthers)

- [4. LIDAR based SLAM](#LIDARSLAM)

	- [4.1 Framework](#LFramework)

	- [4.2 Others](#LOthers)

- [5. Learning based SLAM](#LSLAM)

	- [5.1 Survey](#LSurvey)

	- [5.2 Others](#LOthers5)

	- [5.3 Deep Features](#DeepFeat)

	- [5.4 Semantic SLAM](#Semantic)

- [6. Mobile End SLAM](#MSLAM)

	- [6.1 Visual SLAM](#MV)

	- [6.2 Visual Inertial SLAM](#MVI)

	- [6.3 Augmented Reality](#MAR)

	- [6.4 Others](#MOthers)

- [7. Datasets](#Datasets)

- [8. Tutorials](#Tutorials)

	- [8.1 3D Vision](#3DVision)

	- [8.2 Robotics](#Robotics)

	- [8.3 Lie Algebra and Lie Groups](#Lie)

	- [8.4 Optimization Techniques](#Opt)

	- [8.5 Calibration](#Calibr)

	- [8.6 RANSAC](#RANSAC)

- [9. Selected Blogs](#Blogs)

- [10. Research Groups](#Groups)

- [11. Community](#Community)



------



## 1. Hot SLAM Repos on GitHub

- [Awesome-SLAM: Resources and Resource Collections of SLAM](https://github.com/YoujieXia/Awesome-SLAM)

- [awesome-slam: A curated list of awesome SLAM tutorials, projects and communities.](https://github.com/kanster/awesome-slam)

- [SLAM: learning SLAM,curse,paper and others](https://github.com/liulinbo/slam)

- [A list of current SLAM (Simultaneous Localization and Mapping) / VO (Visual Odometry) algorithms](https://github.com/kafendt/List-of-SLAM-VO-algorithms)

- [awesome-visual-slam: The list of vision-based SLAM / Visual Odometry open source, blogs, and papers](https://github.com/tzutalin/awesome-visual-slam)

- [Lee-SLAM-source: SLAM 开发学习资源与经验分享](https://github.com/AlbertSlam/Lee-SLAM-source)

- [awesome-SLAM-list](https://github.com/OpenSLAM/awesome-SLAM-list)

- [VIO-Resources](https://github.com/hhmaizi/VIO-Resources/blob/master/resources4vio)



------



## 2. Visual SLAM



### 2.1 Framework 

- [OpenVSLAM: A Versatile Visual SLAM Framework](https://github.com/OpenVSLAM-Community/openvslam)

- [OpenSfM: Open source Structure-from-Motion pipeline](https://github.com/mapillary/OpenSfM)

- [GSLAM (A General SLAM Framework and BenchMark)](https://github.com/zdzhaoyong/GSLAM)

- [ScaViSLAM](https://github.com/strasdat/ScaViSLAM)





### 2.2 Monocular

- [ORB_SLAM: A Versatile and Accurate Monocular SLAM ](https://github.com/raulmur/ORB_SLAM)

- [LSD-SLAM: Large-Scale Direct Monocular SLAM](https://github.com/tum-vision/lsd_slam)

- [DSO: Direct Sparse Odometry](https://github.com/JakobEngel/dso)

- [LDSO: Direct Sparse Odometry with Loop Closure](https://github.com/tum-vision/LDSO)

- [SVO: Semi-direct Visual Odometry](https://github.com/uzh-rpg/rpg_svo)

- [PTAM: Parallel Tracking and Mapping](https://github.com/Oxford-PTAM/PTAM-GPL)

- [LPVO: Line and Plane based Visual Odometry](https://github.com/PyojinKim/LPVO)

- [LCSD_SLAM: Loosely-Coupled Semi-Direct Monocular SLAM](https://github.com/sunghoon031/LCSD_SLAM)

- [CCM-SLAM: Robust and Efficient Centralized Collaborative Monocular SLAM for Robotic Teams](https://github.com/VIS4ROB-lab/ccm_slam)





### 2.3 Stereo

- [ORB_SLAM2](https://github.com/raulmur/ORB_SLAM2)

- [ORBSLAM2_with_pointcloud_map](https://github.com/gaoxiang12/ORBSLAM2_with_pointcloud_map)

- [PL-SLAM: a Stereo SLAM System through the Combination of Points and Line Segments](https://github.com/rubengooj/pl-slam)

- [StVO-PL: Stereo Visual Odometry by combining point and line segment features](https://github.com/rubengooj/StVO-PL)

- [PL-SVO](https://github.com/rubengooj/pl-svo)

- [stereo-dso: Direct Sparse Odometry with Stereo Cameras](https://github.com/JiatianWu/stereo-dso)

- [S-PTAM: Stereo Parallel Tracking and Mapping](https://github.com/lrse/sptam), Python implementation: [stereo_ptam](https://github.com/uoip/stereo_ptam)

- [Robust Stereo Visual Odometry](https://github.com/famoreno/stereo-vo)

- [OV²SLAM: A Fully Online and Versatile Visual SLAM for Real-Time Applications](https://github.com/ov2slam/ov2slam)





### 2.4 RGBD

- [Dense Visual Odometry and SLAM](https://github.com/tum-vision/dvo_slam)

- [DVO](https://github.com/tum-vision/dvo):Dense Visual Odometry

- [PlanarSLAM](https://github.com/yanyan-li/PlanarSLAM)

- [badslam](https://github.com/ETH3D/badslam): Bundle Adjusted Direct RGB-D SLAM

- [RESLAM](https://github.com/fabianschenk/RESLAM): A real-time robust edge-based SLAM system

- [VDO-SLAM](https://github.com/halajun/VDO_SLAM): A Visual Dynamic Object-aware SLAM System

- [REVO](https://github.com/fabianschenk/REVO): Robust Edge-based Visual Odometry 





### 2.5 Others

- [CubeSLAM](https://github.com/shichaoy/cube_slam): Monocular 3D Object Detection and SLAM

- [se2lam](https://github.com/izhengfan/se2lam): Visual-Odometric On-SE(2) Localization and Mapping

- [se2clam](https://github.com/izhengfan/se2clam): SE(2)-Constrained Localization and Mapping by Fusing Odometry and Vision

- [BreezySLAM](https://github.com/simondlevy/BreezySLAM): Simple, efficient, open-source package for Simultaneous Localization and Mapping in Python, Matlab, Java, and C++

- [MultiCol-SLAM](https://github.com/urbste/MultiCol-SLAM): a multi-fisheye camera SLAM

- [Event-based Stereo Visual Odometry](https://github.com/HKUST-Aerial-Robotics/ESVO)



------



## 3. Visual Inertial SLAM



### 3.1 Framework 

- [maplab](https://github.com/ethz-asl/maplab): An open visual-inertial mapping framework.

- [ORB-SLAM3](https://github.com/UZ-SLAMLab/ORB_SLAM3): An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM

- [VINS-Fusion](https://github.com/HKUST-Aerial-Robotics/VINS-Fusion): An optimization-based multi-sensor state estimator

- [Kimera](https://github.com/MIT-SPARK/Kimera): an open-source library for real-time metric-semantic localization and mapping

- [OpenVINS](https://github.com/rpng/open_vins): An open source platform for visual-inertial navigation research





### 3.2 Monocular

- [OKVIS](https://github.com/ethz-asl/okvis_ros): Open Keyframe-based Visual-Inertial SLAM (ROS Version)

- [ROVIO](https://github.com/ethz-asl/rovio): Robust Visual Inertial Odometry

- [R-VIO](https://github.com/rpng/R-VIO): Robocentric Visual-Inertial Odometry

- [LARVIO](https://github.com/PetWorm/LARVIO): A lightweight, accurate and robust monocular visual inertial odometry based on Multi-State Constraint Kalman Filter

- [msckf_mono](https://github.com/daniilidis-group/msckf_mono)

- LearnVIORB: Visual Inertial SLAM based on ORB-SLAM2 [(ROS Version)](https://github.com/jingpang/LearnVIORB), [LearnViORB_NOROS (Non-ROS Version)](https://github.com/OpenSLAM/LearnViORB_NOROS/tree/master/master/LearnVIORB_NOROS)

- [PVIO](https://github.com/zju3dv/PVIO): Robust and Efficient Visual-Inertial Odometry with Multi-plane Priors

- [PL-VIO](https://github.com/HeYijia/PL-VIO): monocular visual inertial system with point and line features

- [PL-VINS](https://github.com/cnqiangfu/PL-VINS): Real-Time Monocular Visual-Inertial SLAM with Point and Line Features

- [Adaptive Line and Point Feature-based Visual Inertial Odometry for Robust Localization in Indoor Environments](https://github.com/ankh88324/ALVIO)

- [REBiVO](https://github.com/JuanTarrio/rebvo): Realtime Edge Based Inertial Visual Odometry for a Monocular Camera

- [Co-VINS](https://github.com/qintonguav/Co-VINS): Collaborative Localization for Multiple Monocular Visual-Inertial Systems





### 3.3 Stereo

- [msckf_vio](https://github.com/KumarRobotics/msckf_vio): Robust Stereo Visual Inertial Odometry for Fast Autonomous Flight

- [OKVIS](https://github.com/ethz-asl/okvis): Open Keyframe-based Visual-Inertial SLAM

- [Basalt](https://gitlab.com/VladyslavUsenko/basalt): Visual-Inertial Mapping with Non-Linear Factor Recovery

- [ICE-BA](https://github.com/baidu/ICE-BA): Incremental, Consistent and Efficient Bundle Adjustment for Visual-Inertial SLAM

- [ORBSLAM_DWO](https://github.com/JzHuai0108/ORB_SLAM): stereo + inertial input based on ORB_SLAM

- [VI-Stereo-DSO](https://github.com/RonaldSun/VI-Stereo-DSO)

- [Semi-Dense Direct Visual Inertial Odometry](https://github.com/KumarRobotics/sdd_vio)

- [LearnVIORBnorosgai2](https://github.com/ZuoJiaxing/LearnVIORBnorosgai2): Visual Inertial SLAM based on ORB-SLAM2 (Non-ROS Version)

- [ygz-stereo-inertial](https://github.com/gaoxiang12/ygz-stereo-inertial): a stereo-inertial visual odometry





### 3.4 RGBD





### 3.5 Others

- [X Inertial-aided Visual Odometry](https://github.com/ucla-vision/xivo)



------



## 4. LIDAR based SLAM



### 4.1 Framework

- [Cartographer](https://github.com/cartographer-project/cartographer)

- [LOAM-Livox](https://github.com/hku-mars/loam_livox): A robust LiDAR Odometry and Mapping (LOAM) package for Livox-LiDAR





### 4.2 Others

- [FAST-LIO](https://github.com/hku-mars/FAST_LIO)

- [LOL](https://github.com/RozDavid/LOL): Lidar-only Odometry and Localization in 3D point cloud maps

- [PyICP SLAM](https://github.com/gisbi-kim/PyICP-SLAM): Full-python LiDAR SLAM using ICP and Scan Context

- [LIO-SAM](https://github.com/TixiaoShan/LIO-SAM): Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping

- [LeGO-LOAM](https://github.com/RobustFieldAutonomyLab/LeGO-LOAM): Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain

- [hdl_graph_slam](https://github.com/koide3/hdl_graph_slam): 3D LIDAR-based Graph SLAM

- [A-LOAM](https://github.com/HKUST-Aerial-Robotics/A-LOAM): Advanced implementation of LOAM

- [LIO-mapping: A Tightly Coupled 3D Lidar and Inertial Odometry and Mapping Approach](https://github.com/hyye/lio-mapping)

- [SC-LeGO-LOAM](https://github.com/irapkaist/SC-LeGO-LOAM): LiDAR SLAM: Scan Context + LeGO-LOAM

- [Fast LOAM](https://github.com/wh200720041/floam): Fast and Optimized Lidar Odometry And Mapping for indoor/outdoor localization

- [SuMa](https://github.com/jbehley/SuMa): Surfel-based Mapping using 3D Laser Range Data

- [LINS](https://github.com/ChaoqinRobotics/LINS---LiDAR-inertial-SLAM): LiDAR-inertial-SLAM

- [ISCLOAM](https://github.com/wh200720041/iscloam): Intensity Scan Context based full SLAM implementation for autonomous driving

- [MULLS](https://github.com/YuePanEdward/MULLS): Versatile LiDAR SLAM via Multi-metric Linear Least Square



------



## 5. Learning based SLAM

The SLAM algorithms using conventional methods are listed above by default. The section is to list SLAM algos using learning based methods.





### 5.1 Survey

- [A collection of deep learning based localization models](https://github.com/changhao-chen/deep-learning-localization-mapping)

- [3D-Reconstruction-with-Deep-Learning-Methods](https://github.com/natowi/3D-Reconstruction-with-Deep-Learning-Methods)





### 5.2 Others

- [TLIO](https://github.com/CathIAS/TLIO): Tight Learned Inertial Odometry

- [Unsupervised Learning of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstruction](https://github.com/Huangying-Zhan/Depth-VO-Feat)

- [SuperPoint + ORB_SLAM2](https://github.com/KinglittleQ/SuperPoint_SLAM)

- [VINet](https://github.com/HTLife/VINet): Visual-Inertial Odometry as a Sequence-to-Sequence Learning Problem

- [DeepSFM](https://github.com/weixk2015/DeepSFM): Structure From Motion Via Deep Bundle Adjustment

- [Unsupervised Monocular Visual-inertial Odometry Network](https://github.com/Ironbrotherstyle/UnVIO)

- [Semantic SLAM](https://github.com/hridaybavle/semantic_slam)

- [CNN-DSO](https://github.com/muskie82/CNN-DSO): Direct Sparse Odometry with CNN Depth Prediction

- [CNN-SVO](https://github.com/yan99033/CNN-SVO)

- [KFNet](https://github.com/zlthinker/KFNet): Learning Temporal Camera Relocalization using Kalman Filtering

- [Unsupervised Depth Completion from Visual Inertial Odometry](https://github.com/alexklwong/unsupervised-depth-completion-visual-inertial-odometry)

- [The Perfect Match](https://github.com/zgojcic/3DSmoothNet): 3D Point Cloud Matching with Smoothed Densities

- [Beyond Photometric Loss for Self-Supervised Ego-Motion Estimation](https://github.com/hlzz/DeepMatchVO)

- [M^3SNet](https://github.com/whubaichuan/M3VSNet): Unsupervised Multi-metric Multi-view Stereo Network

- [Deep EKF VIO](https://github.com/lichunshang/deep_ekf_vio)

- [Active Neural SLAM](https://github.com/devendrachaplot/Neural-SLAM)

- [DeepFactors](https://github.com/jczarnowski/DeepFactors)

- [OverlapNet](https://github.com/PRBonn/OverlapNet): Loop Closing for 3D LiDAR-based SLAM

- [SO-Net](https://github.com/lijx10/SO-Net): Self-Organizing Network for Point Cloud Analysis

- [Geometry-Aware Learning of Maps for Camera Localization](https://github.com/NVlabs/geomapnet)

- [DeepV2D](https://github.com/princeton-vl/DeepV2D): Video to Depth with Differentiable Structure from Motion

- [PVN3D](https://github.com/ethnhe/PVN3D): A Deep Point-wise 3D Keypoints Voting Network for 6DoF Pose Estimation

- [DeepMVS](https://github.com/phuang17/DeepMVS): Learning Multi-View Stereopsis

- [Epipolar Transformers](https://github.com/yihui-he/epipolar-transformers)

- [DF-VO](https://github.com/Huangying-Zhan/DF-VO): Depth and Flow for Visual Odometry

- [DeepTAM](https://github.com/lmb-freiburg/deeptam): Deep Tracking and Mapping





### 5.3 Deep Features

- [GCNv2 SLAM](https://github.com/jiexiong2016/GCNv2_SLAM): Real-time SLAM system with deep features

- [FCGF](https://github.com/chrischoy/FCGF): Fully Convolutional Geometric Features: Fast and accurate 3D features for registration and correspondence

- [Deep Image Retrieval](https://github.com/naver/deep-image-retrieval)

- [Key.Net](https://github.com/axelBarroso/Key.Net): Keypoint Detection by Handcrafted and Learned CNN Filters





### 5.4 Semantic SLAM

- [SuMa++](https://github.com/PRBonn/semantic_suma): Efficient LiDAR-based Semantic SLAM

- [DS-SLAM](https://github.com/ivipsourcecode/DS-SLAM)

- [Probabilistic Data Association via Mixture Models for Robust Semantic SLAM](https://github.com/MarineRoboticsGroup/mixtures_semantic_slam)

- [SIVO](https://github.com/navganti/SIVO): Semantically Informed Visual Odometry and Mapping

- [orbslam_semantic_nav_ros, RGBD](https://github.com/MRwangmaomao/semantic_slam_nav_ros)

- [Pop-up SLAM](https://github.com/shichaoy/pop_up_slam): Semantic Monocular Plane SLAM for Low-texture Environments

- [Semantic SLAM using ROS, ORB SLAM, PSPNet101](https://github.com/1989Ryan/Semantic_SLAM)



------



## 6. Mobile End SLAM

The SLAM algorithms running on PC end are listed above by default. The section is to list references and resources for SLAM algo dev on mobile end.





### 6.1 Visual SLAM

- [ORB_SLAM-iOS](https://github.com/ygx2011/ORB_SLAM-IOS)

- [ORB_SLAM2-iOS](https://github.com/ygx2011/ORB_SLAM2-IOS)

- [MobileSLAM](https://github.com/xorthat/MobileSLAM): LSD SLAM on Mobile Phone

- [SLAM_AR_Android](https://github.com/Martin20150405/SLAM_AR_Android)





### 6.2 Visual Inertial SLAM

- [VINS-Mobile](https://github.com/HKUST-Aerial-Robotics/VINS-Mobile): Monocular Visual-Inertial State Estimator on Mobile Phones





### 6.3 Augmented Reality

- [Awesome-ARKit](https://github.com/olucurious/Awesome-ARKit)

- [Awesome-ARCore](https://github.com/olucurious/Awesome-ARCore)

- [MixedRealityToolkit-Unity](https://github.com/microsoft/MixedRealityToolkit-Unity)

- [arcore-android-sdk](https://github.com/google-ar/arcore-android-sdk)

- [OpenARK](https://github.com/augcog/OpenARK)

- [opencv-markerless-AR-Mobile](https://github.com/meiroo/opencv-markerless-AR-Mobile)



#### 6.3.1 Depth Image API with iPhone 7 Plus (or newer)

- [DepthAPISampleForiOS11](https://github.com/fromkk/DepthAPISampleForiOS11)

- [AVDepthCamera](https://github.com/faceowener/AVDepthCamera)

- [ios11-depth-map-test](https://github.com/xiangcong/ios11-depth-map-test)

- [ARCore Depth Lab](https://github.com/googlesamples/arcore-depth-lab): Depth API Samples for Unity

- [AR-Depth](https://github.com/facebookresearch/AR-Depth): Fast Depth Densification for Occlusion-Aware Augmented Reality

- [AR-Depth-cpp](https://github.com/muskie82/AR-Depth-cpp): C++ implementation of Fast Depth Densification for Occlusion-aware Augmented Reality (SIGGRAPH-Asia 2018)





### 6.4 Others

- [Microsoft Computer Vision API](https://github.com/microsoft/Cognitive-Vision-Android): Android Client Library & Sample

- [GPUImage](https://github.com/BradLarson/GPUImage): An open source iOS framework for GPU-based image and video processing



------



## 7. Datasets

- [Awesome SLAM Datasets](https://github.com/youngguncho/awesome-slam-datasets)

- [Awesome Robotics Datasets](https://github.com/sunglok/awesome-robotics-datasets): A collection of useful datasets for robotics and computer vision



- [TUM - Monocular Visual Odometry Dataset](https://vision.cs.tum.edu/data/datasets/mono-dataset), [[code](https://github.com/tum-vision/mono_dataset_code)]

- [ADVIO](https://github.com/AaltoVision/ADVIO): An Authentic Dataset for Visual-Inertial Odometry



------



## 8. Tutorials



- 视觉SLAM十四讲/14 lectures on visual SLAM，[English Version](https://github.com/gaoxiang12/slambook-en)，[中文版](https://github.com/gaoxiang12/slambook)

- [Practice of the SlamBook](https://github.com/leftthomas/SlamBook)

- [GraphSLAM_tutorials_code](https://github.com/HeYijia/GraphSLAM_tutorials_code)

- [SLAM 开发学习资源与经验分享](https://github.com/AlbertSlam/Lee-SLAM-source)

- [Visual SLAM/VIO 算法笔记](https://blog.csdn.net/MulinB/article/details/53421864)





### 8.1 3D Vision

- [An Invitation to 3D Vision: A Tutorial for Everyone](https://github.com/sunglok/3dv_tutorial)

- [Computer Vision/Geometric Fundamentals of SLAM](Misc/Geometric-Fundamentals-of-SLAM.md)



#### 8.1.1 Libs

- [opengv](https://github.com/laurentkneip/opengv)

- [Geometry Central](https://github.com/nmwsharp/geometry-central)

- [vilib](https://github.com/uzh-rpg/vilib): CUDA Visual Library by RPG

- [Vitis Vision Library](https://github.com/Xilinx/Vitis_Libraries/tree/master/vision)

- [OpenGR: A C++ library for 3D Global Registration](https://github.com/STORM-IRIT/OpenGR)

- [OpenCP](https://github.com/norishigefukushima/OpenCP): Computational photography library. The code is parallelized by using SIMD intrinsics and multi-threading.





### 8.2 Robotics

- [RoboticSystemsBook](https://github.com/krishauser/RoboticSystemsBook)

- [MATLABRobotics](https://github.com/AtsushiSakai/MATLABRobotics): MATLAB sample codes for mobile robot navigation

- [Kindr](https://github.com/ANYbotics/kindr): Kinematics and Dynamics for Robotics

- [Sensor Fusion in ROS](https://github.com/methylDragon/ros-sensor-fusion-tutorial): An in-depth step-by-step tutorial for implementing sensor fusion with robot_localization

- [fuse](https://github.com/locusrobotics/fuse): The fuse stack provides a general architecture for performing sensor fusion live on a robot. Some possible applications include state estimation, localization, mapping, and calibration.

- [GPU Computing in Robotics](https://github.com/JanuszBedkowski/gpu_computing_in_robotics)





### 8.3 Lie Algebra and Lie Groups

- [Lie groups for Computer Vision](http://ethaneade.com/lie_groups.pdf)

- [Lie groups for 2D and 3D Transformations](http://ethaneade.com/lie.pdf)

- [Hermite Splines in Lie Groups as Products of Geodesics](http://ethaneade.com/lie_spline.pdf)

- [LieTransformer](https://github.com/anonymous-code-0/lie-transformer)



#### 8.3.1 Libs

- [Sophus](https://github.com/strasdat/Sophus): C++ implementation of Lie Groups using Eigen

- [manif](https://github.com/artivis/manif): A small C++11 header-only library for Lie theory





### 8.4 Optimization Techniques 

- [Gauss-Newton/Levenberg-Marquardt Optimization](http://ethaneade.com/optimization.pdf)

- [How a Kalman filter works, in pictures](http://www.bzarg.com/p/how-a-kalman-filter-works-in-pictures/)

- [卡爾曼濾波 (Kalman Filter)](http://silverwind1982.pixnet.net/blog/post/167680859)

- [翻譯 Understanding the Basis of the Kalman Filter Via a Simple and Intuitive Derivation](http://silverwind1982.pixnet.net/blog/post/171132644)



#### 8.4.1 Libs

- [ceres-solver](https://github.com/ceres-solver/ceres-solver): A large scale non-linear optimization library

- [g2o](https://github.com/RainerKuemmerle/g2o): A General Framework for Graph Optimization

- [GTSAM](https://github.com/borglab/gtsam): Georgia Tech Smoothing and Mapping Library

	- [miniSAM](https://github.com/dongjing3309/minisam): A general and flexible factor graph non-linear least square optimization framework

	- [AprilSAM](https://github.com/xipengwang/AprilSAM): Real-time Smoothing and Mapping

	- [GTSAM Tutorial Examples](https://github.com/dongjing3309/gtsam-examples)

- [AMGCL](https://github.com/ddemidov/amgcl): C++ library for solving large sparse linear systems with algebraic multigrid method

- [Armadillo](https://gitlab.com/conradsnicta/armadillo-code): fast C++ library for linear algebra & scientific computing

- [IFOPT](https://github.com/ethz-adrl/ifopt): An Eigen-based, light-weight C++ Interface to Nonlinear Programming Solvers (Ipopt, Snopt)

- [LBFGS++](https://github.com/yixuan/LBFGSpp): A header-only C++ library for L-BFGS and L-BFGS-B algorithms

- [OptimLib](https://github.com/kthohr/optim): a lightweight C++ library of numerical optimization methods for nonlinear functions

- [PoseLib](https://github.com/vlarsson/PoseLib): a collection of minimal solvers for camera pose estimation

- [fpm](https://github.com/MikeLankamp/fpm): C++ header-only fixed-point math library





### 8.5 Calibration

#### 8.5.1 Libs

- [kalibr](https://github.com/rpng/kalibr_allan): The Kalibr visual-inertial calibration toolbox

	- [kalibr_allan](https://github.com/rpng/kalibr_allan): IMU Allan standard deviation charts for use with Kalibr and inertial kalman filters

- [Accurate geometric camera calibration with generic camera models](https://github.com/puzzlepaint/camera_calibration)

- [LI-Calib](https://github.com/APRIL-ZJU/lidar_IMU_calib): Targetless Calibration of LiDAR-IMU System Based on Continuous-time Batch Estimation

- [Online Photometric Calibration](https://github.com/tum-vision/online_photometric_calibration)

- [IMU-TK](https://github.com/Kyle-ak/imu_tk): Inertial Measurement Unit ToolKit

- [crisp](https://github.com/hovren/crisp): Camera-to-IMU calibration and synchronization toolbox

- [VersaVIS](https://github.com/ethz-asl/versavis): An Open Versatile Multi-Camera Visual-Inertial Sensor Suite





### 8.6 RANSAC

- [RansacLib](https://github.com/tsattler/RansacLib): Template-based implementation of RANSAC and its variants in C++



------



## 9. Selected Blogs

- [The Future of Real-Time SLAM and Deep Learning vs SLAM](http://www.computervisionblog.com/2016/01/why-slam-matters-future-of-real-time.html)

- [IMU Data Fusing: Complementary, Kalman, and Mahony Filter](http://www.olliw.eu/2013/imu-data-fusing/)



------



## 10. Research Groups

TBA



------



## 11. Community

- [PaoPaoRobot - 泡泡机器人](https://github.com/PaoPaoRobot)

- [OpenSLAM.org](https://github.com/OpenSLAM-org)

- [OpenVSLAM-Community](https://github.com/OpenVSLAM-Community)