shumingcong

ai-imu-dr

AI-IMU Dead-Reckoning

3D-Machine-Learning

A resource repository for 3D machine learning

arxiv-sanity-preserver

Web interface for browsing, search and filtering recent arxiv submissions

ccv

C-based/Cached/Core Computer Vision Library, A Modern Computer Vision Library

CNN-SVO

Real-time motion from structure

cpf_localization

Data-Science-Notes

数据科学的笔记以及资料搜集

edgeslam

Edge-SLAM: Edge-Assisted Visual Simultaneous Localization and Mapping

Hierarchical-Localization

Visual localization made easy with hloc

imu_utils

A ROS package tool to analyze the IMU performance.

IMUCalibration-Gesture

calibration for Imu and show gesture

magnetic-field-odometry

Magnetic-Field aided Inertial Navigation System

MAINSvsMAGEKF

MAINS: A Magnetic Field Aided Inertial Navigation System for Indoor Positioning

Map-based-Visual-Localization

A general framework for map-based visual localization. It contains 1) Map Generation which support traditional features or deeplearning features. 2) Hierarchical-Localizationvisual in visual(points or line) map. 3)Fusion framework with IMU, wheel odom and GPS sensors.

Mobile-Pose-Estimation-Pipeline-Prototype

mola

A Modular Optimization framework for Localization and mApping (MOLA)

NBA-Players

ECCV2020 - Official code repository for the paper : Reconstructing NBA Players

note

学习笔记整理📚

open_vins

An open source platform for visual-inertial navigation research.

OpenSfM

Open source Structure from Motion pipeline

openvslam

OpenVSLAM: a Versatile Visual SLAM Framework

pvnet

Code for "PVNet: Pixel-wise Voting Network for 6DoF Pose Estimation" CVPR 2019 oral

Python-1

All Algorithms implemented in Python

python-learning

python_every_day_100examples

scikit-image

Image processing in Python

Terminal

The new Windows Terminal, and the original Windows console host -- all in the same place!

the-art-of-command-line

Master the command line, in one page

VINS-Fusion

An optimization-based multi-sensor state estimator

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

WNN-CNN-GL

Visual Global Localization with a Hybrid WNN-CNN Approach