3D-LIDAR-SLAM-RESOURCE

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3D-LIDAR-SLAM-RESOURCE

★ 11 Open GitHub ↗

This project will collect the 3d_lidar_SLAM open resource, which include VO, Mapping and so on....

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# 3D-LIDAR-SLAM-RESOURCE
I will collect the 3d_lidar_SLAM open resource, which include VO, Mapping and so on....

## Here has five to six paper-codes collected by @topcomma

> Paper and the corresponding source code for LiDAR SLAM

1. <<Real-Time Loop Closure in 2D LIDAR SLAM>> , ICRA 2016 <<Efficient Sparse Pose Adjustment for 2D Mapping>> (SPA) <>(BBS)

**Google Cartographer** implementation to them. Github: https://github.com/googlecartographer/cartographer

2. <<SegMatch Segment based on loop-closure 3D point clouds>> 2017 <<SegMap 3D Segment Mapping using Data-Driven Descriptors>> 2018

SegMap: https://github.com/ethz-asl/segmap

3. <<Efficient Surfel-Based SLAM using 3D Laser Range Data in Urban Environments>>(201806) <>(2017_11)

SuMa: https://github.com/jbehley/SuMa

4. <2017-J. Zhang and S. Singh. **LOAM: Lidar Odometry and Mapping in Real-time**. Robotics: Science and Systems Conference (RSS). Berkeley, CA, July 2014>

**LOAM**: https://github.com/daobilige-su/loam_velodyne

5. <<Joint Self-Localization and Tracking of Generic Objects in 3D Range Data>> <<Interlacing Self-Localization,Moving Object Tracking and Mapping for 3D Range Sensors>>

Tracking Moving Object: https://github.com/FrankMoosmann/FranksVelodyneAlgos

6. Fast segmentation of 3D point clouds: A paradigm on LiDAR data for autonomous vehicle applications

Fast 3D Segmentation: https://github.com/VincentCheungM/Run_based_segmentation (Some innovation not implemented!)

## 3D-LIDAR-SLAM OPEN CODE(based lidar or vlp-16/64 or hokuyo ...)

7. based on NDT registration algorithm. With loop detection and back-end optimization, a map with global consistency can be generated.

**lidar_slam_3d**: https://github.com/ningwang1028/lidar_slam_3d

here is a chinese version: https://github.com/ZHI-ANG/lidar_slam_3d.
  
8. Advanced implementation of LOAM： <J. Zhang and S. Singh. LOAM: Lidar Odometry and Mapping in Real-time>

**A-LOAM:** https://github.com/HKUST-Aerial-Robotics/A-LOAM
  
9. LOAM-Chinese translate.

**LOAM_NOTED**: https://github.com/cuitaixiang/LOAM_NOTED
  
10. <LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain>

**LeGO-LOAM**: https://github.com/RobustFieldAutonomyLab/LeGO-LOAM
  
11. <J. Zhang and S. Singh. LOAM: Lidar Odometry and Mapping in Real-time. Robotics: Science and Systems Conference (RSS). Berkeley, CA, July 2014.>

**loam_velodyne**: https://github.com/laboshinl/loam_velodyne
  
# 2D lidar Mapping Resource (like as rplidar A1-3, Mapper and so on...)

The 2D lidar slam could be dvided into **Filter SLAM** and **Graph-based SLAM**

1. Based ArUco, get the camera pose to achieve EKF-SLAM

**EKF-SLAM**: https://github.com/ydsf16/aruco_ekf_slam

2. FAST-SLAM

**Fast-SLAM:** https://github.com/JoachimClemens/Evidential-FastSLAM

3. GMapping

**Gmapping**: https://github.com/ros-perception/slam_gmapping
 
4. Hector-SLAM

5. KartoSLAM/ LagoSLAM/ LagoSLAM /Cartographer

More information can visit this website: https://zhuanlan.zhihu.com/p/78334136.

3D-LIDAR-SLAM-RESOURCE

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