MaFei's starred repositories
DeepFaceLab
DeepFaceLab is the leading software for creating deepfakes.
Vox-Fusion
Code for "Dense Tracking and Mapping with Voxel-based Neural Implicit Representation", ISMAR 2022
01_all_series_quickstart
Part I videos: Quick Start from weidongshan's Linux video Tutorials
popi_project
Here is everything you need to know about POPI, our open-source quadruped robot. If you want to check the videos we will release about it, you can have a look at our YouTube channel.
WHU-data-science-introduction
武汉大学数据科学导论
quadruped_inno
Simulation of quadruped for Thesis Project
svo_edgelet
A more robust SVO with edgelet feature
LSD-OpenCV-MATLAB
Line Segment Detector for OpenCV, MATLAB, and Python.
Road2Coding
编程之路
guyueclass
古月学院课程代码
VINS-Fusion-AstraPro
对AstraPro相机的适配版
ORB_SLAM3_detailed_comments
Detailed comments for ORB-SLAM3
IndoorMapping
基于ORB-SLAM生成三维密集点云，并使用OctoMap构建室内导航地图。添加八叉树地图转换工具。
legged_control
Nonlinear MPC and WBC framework for legged robot based on OCS2 and ros-controls
awesome-slam-datasets
A curated list of awesome datasets for SLAM
visual_odometry
visual odometry on KITTI dataset, Monocular 2D-2D and Stereo 2D-3D implemented
Monocular-Visual-Odometry
Monocular Visual Odometry on KITTI dataset. Implemented in MATLAB
vo-howard08
[Reimplementation Howard 2008] A MATLAB implementation of Visual Odometry using Andrew Howard's 2008 paper.
Visual-Inertial-Odometry
An implementation and improvement of the MSCKF algorithm for Visual Inertial Odometry for pose estimation of a mobile platform (such as a robot)
KITTI_visual_odometry
Tutorial for working with the KITTI odometry dataset in Python with OpenCV. Includes a review of Computer Vision fundamentals.
Monocular-Visual-Odometry
A simple monocular visual odometry (part of vSLAM) by ORB keypoints with initialization, tracking, local map and bundle adjustment. (WARNING: Hi, I'm sorry that this project is tuned for course demo, not for real world applications !!!)
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