Giter Club home page Giter Club logo

liyang-whu / vo-slam-review Goto Github PK

View Code? Open in Web Editor NEW

This project forked from michaelbeechan/vo-slam-review

0.0 0.0 0.0 126.78 MB

SLAM is mainly divided into two parts: the front end and the back end. The front end is the visual odometer (VO), which roughly estimates the motion of the camera based on the information of adjacent images and provides a good initial value for the back end.The implementation methods of VO can be divided into two categories according to whether features are extracted or not: feature point-based methods, and direct methods without feature points. VO based on feature points is stable and insensitive to illumination and dynamic objects

License: Apache License 2.0

vo-slam-review's Introduction

Visual Odometry（VO）-SLAM-Review

SLAM is mainly divided into two parts: the front end and the back end. The front end is the visual odometer (VO), which roughly estimates the motion of the camera based on the information of adjacent images and provides a good initial value for the back end.The implementation methods of VO can be divided into two categories according to whether features are extracted or not: feature point-based methods, and direct methods without feature points. VO based on feature points is stable and insensitive to illumination and dynamic objects

=======================================================================

github：https://github.com/MichaelBeechan

CSDN：https://blog.csdn.net/u011344545

=======================================================================

SALM review paper download：

https://download.csdn.net/download/u011344545/10850261

1、Visual Odometry or VSLAM

2、Visual Inertial Odometry or VIO-SLAM

3、Based CNN（Net VO or Net VSLAM）

4、Lidar Visual odometry or Lidar SLAM

5、Semanitc SLAM

6、Datasets

7、Libraries

OF-VO：Robust and Efficient Stereo Visual Odometry Using Points and Feature Optical Flow

Code：https://github.com/MichaelBeechan/MyStereoLibviso2

SLAMBook

Paper：14 Lectures on Visual SLAM: From Theory to Practice,

Code：https://github.com/gaoxiang12/slambook

SLAMBook2

Code：https://github.com/gaoxiang12/slambook2

SVO: Fast Semi-Direct Monocular Visual Odometry

Paper：http://rpg.ifi.uzh.ch/docs/ICRA14_Forster.pdf

Video: http://youtu.be/2YnIMfw6bJY

Code：https://github.com/uzh-rpg/rpg_svo

Robust Odometry Estimation for RGB-D Cameras

Real-Time Visual Odometry from Dense RGB-D Images

Paper：http://www.cs.nuim.ie/research/vision/data/icra2013/Whelan13icra.pdf

Code：https://github.com/tum-vision/dvo

Parallel Tracking and Mapping for Small AR Workspaces

Paper：https://cse.sc.edu/~yiannisr/774/2015/ptam.pdf

http://www.robots.ox.ac.uk/ActiveVision/Papers/klein_murray_ismar2007/klein_murray_ismar2007.pdf

Code：https://github.com/Oxford-PTAM/PTAM-GPL

ORBSLAM

Code3：https://github.com/MichaelBeechan/ORBSLAM3

Paper3：ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM：https://arxiv.org/pdf/2007.11898.pdf

Code2：https://github.com/raulmur/ORB_SLAM2

Paper2：ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras：https://arxiv.org/pdf/1610.06475.pdf

Code1：https://github.com/raulmur/ORB_SLAM

Paper1:ORB-SLAM: A Versatile and Accurate Monocular SLAM System:https://arxiv.org/pdf/1502.00956.pdf

A ROS Implementation of the Mono-Slam Algorithm

Paper：https://www.researchgate.net/publication/269200654_A_ROS_Implementation_of_the_Mono-Slam_Algorithm

Code：https://github.com/rrg-polito/mono-slam

DTAM: Dense tracking and mapping in real-time

Paper：https://ieeexplore.ieee.org/document/6126513

Code：https://github.com/anuranbaka/OpenDTAM

LSD-SLAM: Large-Scale Direct Monocular SLAM

Paper：http://pdfs.semanticscholar.org/c13c/b6dfd26a1b545d50d05b52c99eb87b1c82b2.pdf

https://vision.in.tum.de/research/vslam/lsdslam

Code：https://github.com/tum-vision/lsd_slam

RGBD-Odometry (Visual Odometry based RGB-D images)

Real-Time Visual Odometry from Dense RGB-D Images

Code：https://github.com/tzutalin/OpenCV-RgbdOdometry

Paper：http://www.computer.org/csdl/proceedings/iccvw/2011/0063/00/06130321.pdf

Py-MVO: Monocular Visual Odometry using Python

Code：https://github.com/Transportation-Inspection/visual_odometry

Video：https://www.youtube.com/watch?v=E8JK19TmTL4&feature=youtu.be

Stereo-Odometry-SOFT

MATLAB Implementation of Visual Odometry using SOFT algorithm

Code：https://github.com/Mayankm96/Stereo-Odometry-SOFT

Paper：https://ieeexplore.ieee.org/document/7324219

GF_ORB_SLAM：Good Feature Matching: Towards Accurate, Robust VO/VSLAM with Low Latency

Paper：https://arxiv.org/pdf/2001.00714.pdf

Code：https://github.com/ivalab/GF_ORB_SLAM

monoVO-python

Code1：https://github.com/uoip/monoVO-pythone：https://github.com/uoip/monoVO-python

Code2：https://github.com/yueying/LearningVO

DVO：Robust Odometry Estimation for RGB-D Cameras

Code：https://github.com/tum-vision/dvo

https://vision.in.tum.de/data/software/dvo

Paper：https://www.researchgate.net/publication/221430091_Real-time_visual_odometry_from_dense_RGB-D_images

Dense Visual Odometry and SLAM (dvo_slam)

Code：https://github.com/tum-vision/dvo_slam

https://vision.in.tum.de/data/software/dvo

Paper：https://www.researchgate.net/publication/261353146_Dense_visual_SLAM_for_RGB-D_cameras

REVO：Robust Edge-based Visual Odometry

Combining Edge Images and Depth Maps for Robust Visual Odometry

Robust Edge-based Visual Odometry using Machine-Learned Edges

Code：https://github.com/fabianschenk/REVO

Paper：https://graz.pure.elsevier.com/

xivo

X Inertial-aided Visual Odometry

Code：https://github.com/ucla-vision/xivo

Paper：XIVO: X Inertial-aided Visual Odometry and Sparse Mapping

PaoPaoRobot

Code：https://github.com/PaoPaoRobot

ygz-slam

Code：https://github.com/PaoPaoRobot/ygz-slam

https://github.com/gaoxiang12/ygz-stereo-inertial

https://github.com/gaoxiang12/ORB-YGZ-SLAM

https://www.ctolib.com/generalized-intelligence-GAAS.html#5-ygz-slam

RTAB MAP

Online Global Loop Closure Detection for Large-Scale Multi-Session Graph-Based SLAM, 2014 Appearance-Based Loop Closure Detection for Online Large-Scale and Long-Term Operation, 2013

MYNT-EYE

Code：https://github.com/slightech

Kintinuous

Real-time Large Scale Dense RGB-D SLAM with Volumetric Fusion

Deformation-based Loop Closure for Large Scale Dense RGB-D SLAM

Robust Real-Time Visual Odometry for Dense RGB-D Mapping

Kintinuous: Spatially Extended KinectFusion

A method and system for mapping an environment

Code：https://github.com/mp3guy/Kintinuous

ElasticFusion

ElasticFusion: Dense SLAM Without A Pose Graph

ElasticFusion: Real-Time Dense SLAM and Light Source Estimation

Paper：http://www.thomaswhelan.ie/Whelan16ijrr.pdf http://thomaswhelan.ie/Whelan15rss.pdf

Code：https://github.com/mp3guy/ElasticFusion

Co-Fusion：Real-time Segmentation, Tracking and Fusion of Multiple Objects

Paper：http://visual.cs.ucl.ac.uk/pubs/cofusion/index.html

R-VIO：Robocentric Visual-Inertial Odometry

(Kimera-VIO is a Visual Inertial Odometry pipeline for accurate State Estimation from Stereo + IMU data.)

Code：https://github.com/rpng/R-VIO

Paper：https://arxiv.org/abs/1805.04031

Kimera-VIO: Open-Source Visual Inertial Odometry

Code：https://github.com/MIT-SPARK/Kimera-VIO

Paper：https://arxiv.org/abs/1910.02490

Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping

ADVIO: An Authentic Dataset for Visual-Inertial Odometry

Code：https://github.com/AaltoVision/ADVIO

Paper：https://arxiv.org/abs/1807.09828

Data：https://zenodo.org/record/1476931#.XgCvYVIza00

MSCKF_VIO：Robust Stereo Visual Inertial Odometry for Fast Autonomous Flight

Paper：https://arxiv.org/abs/1712.00036

Code：https://github.com/KumarRobotics/msckf_vio

Kimera-VIO: Open-Source Visual Inertial Odometry

Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping

Code：https://github.com/MIT-SPARK/Kimera-VIO

Paper：https://arxiv.org/abs/1910.02490

LIBVISO2: C++ Library for Visual Odometry 2

Paper：http://www.cvlibs.net/software/libviso/

Code：https://github.com/srv/viso2

Stereo Visual SLAM for Mobile Robots Navigation

A constant-time SLAM back-end in the continuum between global mapping and submapping: application to visual stereo SLAM

Paper：http://mapir.uma.es/famoreno/papers/thesis/FAMD_thesis.pdf

Code：https://github.com/famoreno/stereo-vo

Combining Edge Images and Depth Maps for Robust Visual Odometry

Robust Edge-based Visual Odometry using Machine-Learned Edges(REVO)

Paper：https://graz.pure.elsevier.com/

Code：https://github.com/fabianschenk/REVO

HKUST Aerial Robotics Group

VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator

Paper：https://arxiv.org/pdf/1708.03852.pdf

Code：https://github.com/HKUST-Aerial-Robotics/VINS-Mono

VINS-Fusion:Online Temporal Calibration for Monocular Visual-Inertial Systems

Paper：https://arxiv.org/pdf/1808.00692.pdf

Code：https://github.com/HKUST-Aerial-Robotics/VINS-Fusion

Monocular Visual-Inertial State Estimation for Mobile Augmented Reality

Paper：https://ieeexplore.ieee.org/document/8115400

Code：https://github.com/HKUST-Aerial-Robotics/VINS-Mobile

Computer Vision Group TUM Department of Informatics Technical University of Munich

DSO: Direct Sparse Odometry

Code：https://github.com/JingeTu/StereoDSO

Visual-Inertial DSOhttps://vision.in.tum.de/research/vslam/vi-dso

DVSO：https://vision.in.tum.de/research/vslam/dvso

DSO with Loop-closure and Sim(3) pose graph optimization：https://vision.in.tum.de/research/vslam/ldso

Stereo odometry based on careful feature selection and tracking

Paper：https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7324219

Code：https://github.com/Mayankm96/Stereo-Odometry-SOFT

OKVIS: Open Keyframe-based Visual-Inertial SLAM

Code：https://github.com/gaoxiang12/okvis

Trifo-VIO: Robust and Efficient Stereo Visual Inertial Odometry using Points and Lines

Paper：https://arxiv.org/pdf/1803.02403.pdf

Code：https://github.com/UMiNS/Trifocal-tensor-VIO

PL-VIO: Tightly-Coupled Monocular Visual–Inertial Odometry Using Point and Line Features

Paper：https://www.mdpi.com/1424-8220/18/4/1159/html

Overview of visual inertial navigation

A Review of Visual-Inertial Simultaneous Localization and Mapping from Filtering-Based and Optimization-Based Perspectives：

https://ieeexplore.ieee.org/document/5423178

https://www.mdpi.com/2218-6581/7/3/45

VINet: Visual-Inertial Odometry as a Sequence-to-Sequence Learning Problem

Paper：https://arxiv.org/abs/1701.08376

Code：https://github.com/HTLife/VINet

DeepVO: Towards End-to-End Visual Odometry with Deep Recurrent Convolutional Neural Networks

Code：https://github.com/ildoonet/deepvo

https://github.com/sladebot/deepvo

https://github.com/themightyoarfish/deepVO

https://github.com/fshamshirdar/DeepVO (pytorch)

Paper：http://www.cs.ox.ac.uk/files/9026/DeepVO.pdf

UnDeepVO: Implementation of Monocular Visual Odometry through Unsupervised Deep Learning

Code：https://github.com/drmaj/UnDeepVO

Paper：https://arxiv.org/pdf/1709.06841.pdf

SfM-Net: SfM-Net: Learning of Structure and Motion from Video

Code： https://github.com/waxz/sfm_net

Paper： https://arxiv.org/pdf/1704.07804v1.pdf

CNN-SLAM: CNN-SLAM: Real-time dense monocular SLAM with learned depth prediction

Code： https://github.com/iitmcvg/CNN_SLAM

Paper：https://arxiv.org/pdf/1704.03489.pdf

PoseNet: Posenet: A convolutional network for real-time 6-dof camera relocalization(ICCV2015)

Code：https://github.com/alexgkendall/caffe-posenet or https://github.com/kentsommer/tensorflow-posenet

Paper：https://arxiv.org/pdf/1505.07427.pdf or https://arxiv.org/pdf/1509.05909.pdf

VidLoc: VidLoc: 6-doF video-clip relocalization

Code: https://github.com/futurely/deep-camera-relocalization

Paper: https://arxiv.org/pdf/1702.06521.pdf

NetVLAD: NetVLAD: CNN architecture for weakly supervised place recognition(CVPR2016)

Code: https://github.com/Relja/netvlad (Matlab) or https://github.com/lyakaap/NetVLAD-pytorch

Paper: https://arxiv.org/pdf/1511.07247.pdf

DeMoN: Depth and Motion Network for Learning Monocular Stereo（CVPR2017）

Code: https://github.com/lmb-freiburg/demon

Paper: https://arxiv.org/pdf/1612.02401v2.pdf

Learned Stereo Machine

Code: https://github.com/akar43/lsm

Paper: https://arxiv.org/pdf/1708.05375.pdf

SfMLearner: Unsupervised Learning of Depth and Ego-Motion from Video

Code: https://github.com/tinghuiz/SfMLearner

Paper: https://arxiv.org/pdf/1704.07813.pdf

Toward Geometric Deep SLAM

Code: UNopen(https://github.com/mtrasobaresb)

Paper: https://arxiv.org/pdf/1707.07410v1.pdf

Neural SLAM : Learning to Explore with External Memory

Code: UNopen

Paper: https://arxiv.org/pdf/1706.09520.pdf

PoseConvGRU: A Monocular Approach for Visual Ego-motion Estimation by Learning(2019)

Code: UNopen

Paper: https://arxiv.org/pdf/1906.08095.pdf

Semi-Dense 3D Semantic Mapping from Monocular SLAM(2016)

Code: UNopen

Paper: https://arxiv.org/pdf/1611.04144.pdf

Image-based 3D Object Reconstruction: State-of-the-Art and Trends in the Deep Learning Era(2019)

Paper: https://arxiv.org/pdf/1906.06543.pdf

DeepMVS: DeepMVS: Learning Multi-view Stereopsis(CVPR2018)

Code: https://github.com/phuang17/DeepMVS

Paper: https://phuang17.github.io/DeepMVS/index.html

Paper: https://arxiv.org/pdf/1804.00650.pdf

MVSNet: Mvsnet: Depth inference for unstructured multi-view stereo(ECCV2018)

Code1: https://github.com/YoYo000/MVSNet

Code2: https://github.com/YoYo000/BlendedMVS

Paper: https://arxiv.org/pdf/1804.02505.pdf

PointMVSNet:Point-based Multi-view Stereo Network

Code: https://github.com/callmeray/PointMVSNet

Paper: https://arxiv.org/pdf/1908.04422.pdf

Recurrent MVSNet: Recurrent MVSNet for High-resolution Multi-view Stereo Depth Inference(CVPR2019)

Code: https://github.com/YoYo000/MVSNet

Paper: https://arxiv.org/pdf/1902.10556.pdf

(ESP-VO) End-to-End, Sequence-to-Sequence Probabilistic Visual Odometry through Deep Neural Networks

Code: https://github.com/espnet/espnet

https://www.seas.upenn.edu/~meam620/slides/kinematicsI.pdf

Lidar Visual odometry

Lidar-Monocular Visual Odometry

Code：https://github.com/johannes-graeter/limo

Paper：https://arxiv.org/pdf/1807.07524.pdf

RGBD and LIDAR

CAE-LO: LiDAR Odometry Leveraging Fully Unsupervised Convolutional Auto-Encoder for Interest Point Detection and Feature Description

Paper：https://arxiv.org/ftp/arxiv/papers/2001/2001.01354.pdf

Code：https://github.com/SRainGit/CAE-LO

Other open source projects

DynaSLAM A SLAM system robust in dynamic environments for monocular, stereo and RGB-D setups

openvslam A Versatile Visual SLAM Framework

cartographer

Code：https://github.com/googlecartographer/cartographer

Paper：https://google-cartographer.readthedocs.io/en/latest/

A-LOAM（Advanced implementation of LOAM）

LOAM: Lidar Odometry and Mapping in Real-time

Code1：https://github.com/HKUST-Aerial-Robotics/A-LOAM

Code2：https://github.com/cuitaixiang/LOAM_NOTED

Paper：http://roboticsproceedings.org/rss10/p07.pdf

SemanticFusion: Dense 3D semantic mapping with convolutional neural networks

Code: https://github.com/seaun163/semanticfusion

Paper: https://arxiv.org/pdf/1609.05130v2.pdf

ORB_SLAM2_SSD_Semantic

Code：https://github.com/Ewenwan/ORB_SLAM2_SSD_Semantic

Visual Semantic SLAM with Landmarks for Large-Scale Outdoor Environment

Paper：https://arxiv.org/pdf/2001.01028.pdf

Code：https://github.com/1989Ryan/Semantic_SLAM/

Datasets

Libraries

Basic vision and trasformation libraries

Thread-safe queue libraries

Loop detection

Graph Optimization

Map library

Tools

vo-slam-review's People

Contributors

michaelbeechan avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.