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weichuanpan's Projects

graphgnsslib icon graphgnsslib

An Open-source Package for GNSS Positioning and Real-time Kinematic Using Factor Graph Optimization

gvins icon gvins

Tightly coupled GNSS-Visual-Inertial system for locally smooth and globally consistent state estimation in complex environment.

gvins-dataset icon gvins-dataset

A dataset containing synchronized visual, inertial and GNSS raw measurements.

ic-gvins icon ic-gvins

A Robust, Real-time, INS-Centric GNSS-Visual-Inertial Navigation System

kalibr icon kalibr

The Kalibr visual-inertial calibration toolbox

kalibr_allan icon kalibr_allan

IMU Allan standard deviation charts for use with Kalibr and inertial kalman filters.

m2dgr icon m2dgr

M2DGR： a Multi-modal and Multi-scenario Dataset for Ground Robots

msckf_mono icon msckf_mono

VIO_Learning. A simple implement of mono msckf in https://github.com/TurtleZhong/msckf_mono/tree/master/src/msckf_mine_1.0

ndt-library icon ndt-library

These is ndt library for ndt_mapping and ndt_localization

openmvg icon openmvg

open Multiple View Geometry library. Basis for 3D computer vision and Structure from Motion.

os icon os

Writing & Making Operating System and Kernel parts so simple like Hello World Programs, Starting from writing Bootloaders, Hello World Kernel, GDT, IDT, Terminal, Keyboard/Mouse, Memory Manager, HDD ATA R/W, VGA/VESA Graphics

rtklib icon rtklib

A version of RTKLIB optimized for single and dual frequency low cost GPS receivers, especially u-blox receivers. It is based on RTKLIB 2.4.3 and is kept reasonably closely synced to that branch. Documentation for RTKLIB is available at rtklib.com. This software is provided “AS IS” without any warranties of any kind so please be careful, especially if using it in any kind of real-time application. Binaries and tutorials for this code, and sample GPS data sets at :

solveaxxb icon solveaxxb

A C++ library to solve equation AX=XB, which is known as been widely used in hand-eye calibration for robotics

tarl icon tarl

TARL: Temporal Consistent 3D LiDAR Representation Learning for Semantic Perception in Autonomous Driving

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