Kyriema's repositories
ailearning
AiLearning：数据分析+机器学习实战+线性代数+PyTorch+NLTK+TF2
awesome-gnss
Community list of open-source GNSS software and resources :satellite:
d2l-zh
《动手学深度学习》：面向中文读者、能运行、可讨论。中英文版被55个国家的300所大学用于教学。
eagleye
Precise localization based on GNSS and IMU.
FGI-GSRx
FGI-GSRx Open Source multi-GNSS software receiver
ginan
The Australian Government, through Positioning Australia (part of Geoscience Australia), is funding the design, development and operational service of a Global Navigation Satellite System (GNSS) position correction system - the Ginan service and toolkit. The application of the Ginan correction service by a GNSS device has the potential to increase positioning accuracy from meters to centimetres across Australia. The suite of software systems in this repository (the Ginan toolkit) will be used to create the service. It is available now under an open source licence. Ginan will give individuals and organisations no-cost access to the Ginan software and service as a public good.
gLAB
An unofficial mirror of the GNSS-Lab Tool (gLAB) suite
gnss_lib_py
Modular Python tool for parsing, analyzing, and visualizing Global Navigation Satellite Systems (GNSS) data and state estimates
GraphGNSSLib
An Open-source Package for GNSS Positioning and Real-time Kinematic Using Factor Graph Optimization
GVINS
Tightly coupled GNSS-Visual-Inertial system for locally smooth and globally consistent state estimation in complex environment.
hdl_graph_slam
3D LIDAR-based Graph SLAM
imu_utils
A ROS package tool to analyze the IMU performance.
imu_x_fusion
IMU + X(GNSS, 6DoF Odom) Loosely-Coupled Fusion Localization based on ESKF, IEKF, UKF(UKF/SPKF, JUKF, SVD-UKF) and MAP
inertiallabs_gnss_driver
ROS GNSS/INS driver for Inertial Labs positioning systems for the CARMA Platform
laika
Simple Python GNSS processing library
LVI-SAM
LVI-SAM: Tightly-coupled Lidar-Visual-Inertial Odometry via Smoothing and Mapping
OB_GINS
Optimization-Based GNSS/INS Integrated Navigation System
open_vins
An open source platform for visual-inertial navigation research.
ORB_SLAM3_detailed_comments
Detailed comments for ORB-SLAM3
r2live
R2LIVE is a robust, real-time tightly-coupled multi-sensor fusion framework, which fuses the measurement from the LiDAR, inertial sensor, visual camera to achieve robust, accurate state estimation.
r3live
A Robust, Real-time, RGB-colored, LiDAR-Inertial-Visual tightly-coupled state Estimation and mapping package
Recent-Stars-2022
🔥🔥🔥SLAM, Pose/Object tracking, Depth/Disparity/Flow Estimation, 3D-graphic, etc. related papers and code
Road2Coding
编程之路
rtklib-py
Python implementation of RTKLIB. Based on demo5 version. Currently only supports PPK solutions
Visual-Selective-VIO
Code for "Efficient Deep Visual and Inertial Odometry with Adaptive Visual Modality Selection", ECCV 2022