我的导航学习笔记，内容涵盖导航定位开源程序的源码解读 ( 包括：RTKLIB、GAMP、GINav、Ginan、PSINS、SoftGNSS、KF-GINS、GICI-Lib 等)、开源项目梳理、书籍讲义、博客翻译、教程讲座推荐；本仓库会长期更新，分享出来，既是跟大家做个交流，也激励着自己坚持学下去；所有内容都可以随意转载，原始文件都放在这了，欢迎在我的基础上整理出自己的一些文档。

Online Monocular Lane Mapping Using Catmull-Rom Spline (IROS 2023)

Thread pool implementation using c++11 threads

A Lidar-Inertial State Estimator for Robust and Efficient Navigation based on iterated error-state Kalman filter

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

深度学习经典、新论文逐段精读

LiDAR SLAM: BoW3D (22' RA-L) + Scan Context (18 IROS) + LeGO-LOAM (18 IROS)

Map matcher based on hmm solved with viterbi algorithm

误差状态卡尔曼ESKF滤波器融合GPS和IMU，实现更高精度的定位

Real-Time SLAM for Monocular, Stereo and RGB-D Cameras, with Loop Detection and Relocalization Capabilities

一些经典的SLAM算法学习并注释的版本

SLAM 开发学习资源与经验分享

[3DV 2021] DSP-SLAM: Object Oriented SLAM with Deep Shape Priors

SuperPoint + ORB_SLAM2

Inverse Perspective Mapping

Apollo notes (Apollo学习笔记) - Apollo learning notes for beginners.

Joplin - an open source note taking and to-do application with synchronisation capabilities for Windows, macOS, Linux, Android and iOS.

LibCity: An Open Library for Urban Spatial-temporal Data Mining

The map matching functionality is now located in the main repository https://github.com/graphhopper/graphhopper#map-matching

IMU + X(GNSS, 6DoF Odom) Loosely-Coupled Fusion Localization based on ESKF, IEKF, UKF(UKF/SPKF, JUKF, SVD-UKF) and MAP

Intensity-SLAM: Intensity Assisted Localization and Mapping for Large Scale Environment RA-L 2021

🚀 SLAM for autonomous planetary rovers with global localization

Learn ORBSLAM2 and divide the source code into many parts according to their function which can be easily built by the learner from my blog.

CUDA based Iterative Closest Point Algorithm Implementation

TensorFlow Implementation for Computing a Semantically Segmented Bird's Eye View (BEV) Image Given the Images of Multiple Vehicle-Mounted Cameras.

iterative closest point

Robust Point Cloud Registration Using Iterative Probabilistic Data Associations ("Robust ICP")

LiDAR Iris for Loop-Closure Detection(IROS 2020)

R2LIVE: A Robust, Real-time, LiDAR-Inertial-Visual tightly-coupled state Estimator and mapping package

Object (e.g Pedestrian, vehicles) tracking by Extended Kalman Filter (EKF), with fused data from both lidar and radar sensors.