hpstu Goto Github PK

awesome-visual-slam

:books: The list of vision-based SLAM / Visual Odometry open source, blogs, and papers

cpp-learning

cv-arxiv-daily

🎓Automatically Update CV Papers Daily using Github Actions (Update Every 12th hours)

cvpr2021-paper-code-interpretation

cvpr2021/cvpr2020/cvpr2019/cvpr2018/cvpr2017 论文/代码/解读/直播合集，极市团队整理

cvpr2021-papers-with-code

CVPR 2021 论文和开源项目合集

deep-homography-estimation-pytorch

Deep homography network with Pytorch

deep-learning-localization-mapping

A collection of deep learning based localization models

deep-learning-papers-translation

深度学习论文翻译，包括分类论文，检测论文等

deepslam

df-vo

光流法

evo

Python package for the evaluation of odometry and SLAM

hpstu.github.io

个人博客

icra2021-slam-paper-list

mvision

机器人视觉 移动机器人 VS-SLAM ORB-SLAM2 深度学习目标检测 yolov3 行为检测 opencv PCL 机器学习 无人驾驶

obnote

orb-slam2-chinese

:cyclone:中文注释版ORB-SLAM2

orb_slam3

ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM

orb_slam3_detailed_comments

Detailed comments for ORB-SLAM3

orbslam2_detailed_comments

Detailed comments for ORB-SLAM2 with trouble-shooting, key formula derivation, and diagrammatic drawing

ov2slam

OV²SLAM is a Fully Online and Versatile Visual SLAM for Real-Time Applications

pangolin_tutorial

Pangolin中文样例

pcl-learning

🔥PCL（Point Cloud Library）点云库学习记录

pytorch_tutorial

《Pytorch模型训练实用教程》中配套代码

recent-stars-2021

🔥🔥🔥SLAM, Pose/Object tracking, Depth/Disparity/Flow Estimation, 3D-graphic, etc. related papers and code

stereo-transformer

Official Repo for Stereo Transformer: Revisiting Stereo Depth Estimation From a Sequence-to-Sequence Perspective with Transformers.

supergluepretrainednetwork

SuperGlue: Learning Feature Matching with Graph Neural Networks (CVPR 2020, Oral)

superpointpretrainednetwork

PyTorch pre-trained model for real-time interest point detection, description, and sparse tracking (https://arxiv.org/abs/1712.07629)

tips

一些Tips

visual_slam_related_research

视觉(语义) SLAM 相关研究跟踪

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