Paper-and-the-corresponding-source-code-for-LiDAR-SLAM

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
   <<LOAM: LiDAR Odometry and Mapping in Real-Time>> 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!)