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From SLAM to Situational Awareness: Challenges and Survey(2021)
Keyframe-based monocular SLAM：design, survey, and future directions(2018)
RGB-Depth SLAM Review(2018)
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age(2017)
The Revisiting Problem in Simultaneous Localization and Mapping: A Survey on Visual Loop Closure Detection(2022)
Advances in inference and representation for simultaneous localization and mapping(2021)
2.Hardware implementation of SLAM algorithms: a survey on implementation approaches and platforms
3.Deep Learning for Inertial Positioning: A Survey
4.Active SLAM: A Review On Last Decade
Visual SLAM: What are the Current Trends and What to Expect?
MonoSLAM Real-time single camera SLAM(PAMI 2007)
Parallel tracking and mapping for small ar workspaces (2007ISMAR)
DTAM: Dense tracking and mapping in real-time
3D mapping with an RGB-D camera(2013 TRO)
SLAM++ Simultaneous Localisation and Mapping at the Level of Objects(CVPR 2013)
SVO Fast semi-direct monocular visual odometry(ICRA 2014) 7. LSD-SLAM Large-scale direct monocular SLAM
ORB-SLAM a versatile and accurate monocular SLAM system（TOR 2015）
Orb-slam2 An open-source slam system for monocular, stereo, and rgb-d cameras（TRO 2017）
Orb-slam3 An accurate open-source library for visual, visual–inertial, and multimap slam（TRO 2021）
CNN-SLAM Real-time dense monocular SLAM with learned depth prediction（CVPR 2017）
Direct_Sparse_Odometry
NeRF-SLAM: Real-Time Dense Monocular SLAM with Neural Radiance Fields
Role of Deep Learning in Loop Closure Detection for Visual（2021综述）
Comparison of camera-based and 3D LiDAR-based loop closures across weather conditions(2020)
Compressed holistic convnet representations for detecting loop closures in dynamic environments(2020)
Condition-Invariant Multi-View Place Recognition(2019)
Loop Closure Detection Based on Multi-Scale Deep Feature Fusion（2019）
Lightweight unsupervised deep loop closure(2018)
Single-View Place Recognition under Seasonal Changes(2018)
A comparative analysis of sift, surf, kaze, akaze, orb, and brisk(2018)
Image matching using SIFT, SURF, BRIEF and ORB performance comparison for distorted images（2017）
Features from accelerated segment test (fast)（2014）
ORB An efficient alternative to SIFT or SURF（2011）
Brief Binary robust independent elementary features（2010）
Surf Speeded up robust features（2006）
Distinctive image features from scale-invariant key-points（2004）
Visual-SLAM Classical Framework and Key Techniques: A Review
A review of visual SLAM methods for autonomous driving vehicles
A Comprehensive Review on Autonomous Navigation
Event-based Simultaneous Localization and Mapping: A Comprehensive Survey
A Comparative Survey of LiDAR-SLAM and LiDAR based Sensor Technologies
A Comparative Analysis of LiDAR SLAM-based Indoor Navigation for Autonomous Vehicles
Lidar SLAM for Autonomous Driving Vehicles
PFilter: Building Persistent Maps through Feature Filtering for Fastand Accurate LiDAR-based SLAM
A Review of Multi-Sensor Fusion SLAM Systems Based on 3D LIDAR
A Review of Visual-LiDAR Fusion based Simultaneous Localization and Mapping
LI-SLAM Fusing LiDAR and Infrared Camera for Simultaneous Localization and Mapping
A new EKF SLAM algorithm of lidar-based AGV fused with bearing information
A Simultaneous Localization and Mapping (SLAM) Framework for 2.5D Map Building Based on Low-Cost LiDAR and Vision Fusion
MCS-SLAM Multi-Cues Multi-Sensors Fusion SLAM
Localization and Mapping for UGV in Dynamic Scenes with Dynamic Objects Eliminated
When Geometry is not Enough:Using Reflector Markers in Lidar SLAM
3D LiDAR SLAM Integration with GPS/INS for UAVs in Urban GPS-Degraded Environments
An Integrated GNSS/INS/LiDAR-SLAM Positioning Method for Highly Accurate Forest Stem Mapping
SuMa++: Efficient LiDAR-based Semantic SLAM 6. ART-SLAM Accurate Real-Time 6DoF LiDAR SLAM
Ground-SLAM: Ground Constrained LiDAR SLAM for Structured Multi-Floor Environments
Elasticity Meets Continuous-Time: Map-Centric Dense 3D LiDAR SLAM
Real-Time Loop Closure in 2D LIDAR SLAM
OverlapNet Loop Closing for LiDAR-based SLAM
A LiDAR-Visual SLAM Backend with Loop Closure Detection and Graph Optimization
Fast Closed-Loop SLAM based on the fusion of IMU and Lidar
Have I Seen This Place Before A Fast and Robust Loop Detection and Correction Method for 3D Lidar SLAM
LCDNet Deep Loop Closure Detection and Point Cloud Registration for LiDAR SLAM
Cartographer glass: 2D Graph SLAM Framework using LiDAR for Glass Environments
maplab 2.0 – A Modular and Multi-Modal Mapping Framework
MAROAM: Map-based Radar SLAM through Two-step Feature Selection
ORB-SLAM2
ORB-SLAM3
###3.视觉惯性SLAM
VINS-Mono
VINS-Fusion
LOAM
Lego-LOAM
A-LOAM
M-LOAM（多激光雷达）
LIO-SAM（激光惯性）
LVI-SAM（激光惯性视觉）
FAST-LIO（激光惯性）
FAST-LIO2（激光惯性）
R2LIVE（激光视觉惯性）
R3LIVE（激光视觉惯性）
R3LIVE++（激光视觉惯性）