LiDAR SLAM 논문 리스트

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이 글의 출처는 Giseop Kim 님의 Notion 글입니다. 그냥 다 그대로 가져온 수준이에요(…)

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Paper List

  • 1987 PAMI Least-squares fitting of two 3D point sets
    • SVD-based closed form of registration; a basic of basic of the scan matching
  • 92 PAMI A method for registration of 3-D shapes
    • ICP - a basic of the scan matching
  • 97 AR Globally Consistent Range Scan Alignment for Environment Mapping
    • 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
    • NDT registration
  • 06 IJRR Square Root SAM: Simultaneous Localization and Mapping via Square Root Information Smoothing
    • 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
    • 3D version of NDT registration
  • 08 TRO iSAM: Incremental Smoothing and Mapping
    • incremental SAM and an open source library
  • 09 ICRA Real-Time Correlative Scan Matching
    • prof. Olson; later affects to Cartographer, etc
  • 09 ICRA Fast Point Feature Histograms (FPFH) for 3D Registration
    • FPFH (the most famous 3D local descriptor) registration
  • 09 RSS Generalized-ICP
    • uncertainty-embedded ICP (probabilistic perspective)
  • 10 ITSM A Tutorial on Graph-Based SLAM
    • Grisetti’s must-read tutorial
  • 11 IV Velodyne SLAM
    • 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
    • mobile mapping system and IMU fusion
  • 12 RAM Tutorial: Point Cloud Library: Three-Dimensional Object Recognition and 6 DOF Pose Estimation
    • PCL tutorial, but not much delved into the SLAM perspective.
  • 12 IJRR iSAM2: Incremental smoothing and mapping using the Bayes tree
    • 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
    • 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
    • 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
    • a.k.a the open library: Libpointmatcher*
  • 14 RSS LOAM: Lidar Odometry and Mapping in Real-time
    • THE LOAM; (surface and corner) feature matching for frame-to-frame registration and frame-to-map refinement
  • 15 ICRA Visual-lidar Odometry and Mapping: Low-drift, Robust, and Fast
    • Visual + LOAM
  • 15 ICRA Initialization Techniques for 3D SLAM: a Survey on Rotation Estimation and its Use in Pose Graph Optimization
    • LiDAR SLAM usually have more opportunity to think of the better pose graph optimization because it directly measures the depth (rather easier front-end than visual domain).
  • 15 RAM Registration with the Point Cloud Library: A Modular Framework for Aligning in 3-D
    • PCL tutorial for registration
  • 15 IROS [NICP: Dense normal based point cloud registration
    • as already in the title, dense normal](http://jacoposerafin.com/wp-content/uploads/serafin15iros.pdf)
  • 16 ICRA Real-time loop closure in 2D LIDAR SLAM
    • the Google Cartographer’s paper
  • 16 SSRR ICP-based pose-graph SLAM
    • an almost standard framework of scan matching- and pose-graph-based LiDAR SLAM
  • 16 IROS M2DP: A novel 3D point cloud descriptor and its application in loop closure detection
    • place descriptor using a single LiDAR scan
  • 16 BookChapter World modeling
    • various representations of a surrounding environment; selecting a proper representation of the environment is important and it determines the state estimation ways.
  • 18 RSS Efficient Surfel-Based SLAM using 3D Laser Range Data in Urban Environments
    • a.k.a SuMa; projective view rendering, and open source
  • 18 ICRA IMLS-SLAM: scan-to-model matching based on 3D data
    • sophisticated feature selections, not real-time but for the accuracy, this is considered as a SOTA
  • 18 ICRA Elastic LiDAR Fusion: Dense Map-Centric Continuous-Time SLAM
    • (continuous-time) non-rigid map deformation (see 15 RSS ElasticFusion also)
  • 18 ICRA Efficient Continuous-time SLAM for 3D Lidar-based Online Mapping
    • a hierarchical continuous-time LiDAR SLAM
  • 18 IROS LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain
    • range image-based fast feature selection for LOAM; and open source
  • 18 IROS LIPS: LiDAR-Inertial 3D Plane SLAM
    • leveraging plane for LINS system
  • 18 IROS Scan Context: Egocentric Spatial Descriptor for Place Recognition Within 3D Point Cloud Map
    • A visibility-based place descriptor for fast and robust place recognition, and open source
  • 19 A-LOAM (code only)
    • a well-implemented LOAM algorithm (the original LOAM author closed the official code), and open source
  • 19 ICRA Tightly Coupled 3D Lidar Inertial Odometry and Mapping
    • a.k.a lio-mapping; imu tight fusion but practically slow, and open source
  • 19 IV DeLiO: Decoupled LiDAR Odometry
    • rotation and translation are decoupled
  • 19 IJRR SegMap: Segment-based mapping and localization using data-driven descriptors
    • deep segment feature learning for LiDAR place recognition
  • 19 IROS SuMa++: Efficient LiDAR-based Semantic SLAM
    • merging semantic information into SuMa
  • 20 AR DVL-SLAM: sparse depth enhanced direct visual-LiDAR SLAM
    • enhanced visual SLAM by LiDAR data
  • 20 RSS OverlapNet: Loop Closing for LiDAR-based SLAM
    • learning two scan’s overlap and integrated it into the modern probabilistic SLAM system.
  • 20 IROS LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping
    • IMU fusion (tightly) of LOAM, and open source.
  • 20 IROS SpoxelNet: Spherical Voxel-based Deep Place Recognition for 3D Point Clouds of Crowded Indoor Spaces
    • Deep LiDAR feature learning for place recognition and robust to occlusions
  • 20 IROS Semantic Graph Based Place Recognition for 3D Point Clouds
    • Summarizing a place with a single semantic graph. The matching part is also deep (SegMap didn’t).
  • 20 IROS A Fast and Robust Place Recognition Approach for Stereo Visual Odometry Using LiDAR Descriptors
    • LiDAR descriptors are also good for stereo-camera-based place recognition