Selected papers (to do list)

• 87 PAMI Least-squares fitting of two 3D point sets

  tldr: SVD-based closed form of registration; a basic of basic of the scan matching

• 92 PAMI A method for registration of 3-D shapes

  tldr: a.k.a ICP; a basic of the scan matching

• 97 AR Globally Consistent Range Scan Alignment for Environment Mapping

  tldr: mostly called as "Lu and Milios"; considered as the first work of a scan matching and pose graph optimization-based SLAM.

• 03 IROS The Normal Distributions Transform: A New Approach to Laser Scan Matching

  tldr: NDT registration

• 06 IJRR Square Root SAM: Simultaneous Localization and Mapping via Square Root Information Smoothing

  tldr: from probabilistic nature to least square formulation of SLAM for smoothing (i.e., modification of past poses)

• 07 JFR Scan registration for autonomous mining vehicles using 3D-NDT

  tldr: 3D version of NDT registration

• 08 TRO iSAM: Incremental Smoothing and Mapping

  tldr: incremental SAM and an open source library

• 09 ICRA Real-Time Correlative Scan Matching

  tldr: prof. Olson; later affects to Cartographer, etc.

• 09 ICRA Fast Point Feature Histograms (FPFH) for 3D Registration

  tldr: FPFH (the most famous 3D local descriptor) registration

• 09 RSS Generalized-ICP

  tldr: uncertainty-embedded ICP (probabilistic perspective)

• 10 ITSM A Tutorial on Graph-Based SLAM

  tldr: Grisetti's must-read tutorial

• 11 IV Velodyne SLAM

  tldr: an early work of modern 3D scanning LiDAR-based motion estimation

• 12 TRO Zebedee: Design of a Spring-Mounted 3-D Range Sensor with Application to Mobile Mapping

  tldr: mobile mapping system and IMU fusion

• 12 RAM Tutorial: Point Cloud Library: Three-Dimensional Object Recognition and 6 DOF Pose Estimation

  tldr: PCL tutorial, but not much delved into the SLAM perspective.

• 12 IJRR iSAM2: Incremental smoothing and mapping using the Bayes tree

  tldr: in GTSAM 4.0, iSAM2 (not iSAM1) is currently a de-facto default factor graph optimizer.

• 13 ICRA Robust Odometry Estimation for RGB-D Cameras

  tldr: a.k.a DVO; this is not an actually LiDAR thing, but to understand the effectiveness of direct alignment rather ICP

• 13 IROS Dense Visual SLAM for RGB-D Cameras

  tldr: a SLAM version (i.e., including loop closures) of the DVO; studying RGB-D SLAMs is also worthy for LiDAR guys because they frequently considers the both a photometric error and a geometric error.

• 13 AR Challenging data sets for point cloud registration algorithms

  tldr: a.k.a the open library: Libpointmatcher