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For anyone who wants to do research about 3D point cloud based place recognition / loop closure detection.Thanks.

out.: outdoor   |  ind.: indoor  
pc.: point cloud   |  img.: image   |  rad.: radar  
pos.: pose  

Statistics: 🔥 code is available & stars >= 100  |  ⭐ citation >= 50

• [ICRA] Appearance-Based Loop Detection from 3D Laser Data Using the Normal Distributions Transform. [out.pc.] ⭐
• [JFR] Automatic appearance-based loop detection from three-dimensional laser data using the normal distributions transform. [out.pc.] ⭐

• [ICRA] Robust place recognition for 3D range data based on point features. [out.pc.pos.] ⭐

• [SSRR] Loop closure detection using small-sized signatures from 3D LIDAR data. [out.pc.]

• [UST] Real-time lidar-based place recognition using distinctive shape descriptors. [out.pc.img.pos.]
• [TIM] 3-D-Laser-Based Scene Measurement and Place Recognition for Mobile Robots in Dynamic Indoor Environments. [ind.pc.] ⭐

• [ICRA] Place recognition using keypoint voting in large 3D lidar datasets. [out.pc.] ⭐

• [IROS] A Fast Histogram-Based Similarity Measure for Detecting Loop Closures in 3-D LIDAR Data. [code] [out.pc.]

• [IROS] M2DP: A novel 3D point cloud descriptor and its application in loop closure detection. [matlab] [python] [out.pc.] ⭐

• [ICRA] SegMatch: Segment based place recognition in 3D point clouds. [code] [out.pc.pos.] ⭐

• [IROS] Scan Context: Egocentric Spatial Descriptor for Place Recognition Within 3D Point Cloud Map. [code] [out.pc.pos.] 🔥 ⭐
• [IROS] Stabilize an Unsupervised Feature Learning for LiDAR-based Place Recognition. [out.pc. ]
• [IROS] Seeing the Wood for the Trees: Reliable Localization in Urban and Natural Environments. [out.pc.pos.]
• [ICRA] DELIGHT: An Efficient Descriptor for Global Localisation using LiDAR Intensities. [out.pc. ]
• [IV] LocNet: Global Localization in 3D Point Clouds for Mobile Vehicles. [model] [code] [out.pc.pos.]
• [RSS] SegMap: 3D Segment Mapping using Data-Driven Descriptors. [code] [out.pc.] 🔥 ⭐
• [RA-L] Incremental Segment-Based Localization in 3D Point Clouds. [code] [out.pc.] 🔥
• [CVPR] PointNetVLAD: Deep Point Cloud Based Retrieval for Large-Scale Place Recognition. [code] [out.pc.] 🔥 ⭐
• [Sensors] Have I Seen This Place Before? A Fast and Robust Loop Detection and Correction Method for 3D Lidar SLAM. [code] [out.pc.]
• [Sensors] Robust Place Recognition and Loop Closing in Laser-Based SLAM for UGVs in Urban Environments. [out.pc.]

• [RA-L] 1-Day Learning, 1-Year Localization: Long-Term LiDAR Localization Using Scan Context Image. [code] [out.pc.pos.] 🔥

• [RA-L] Local Descriptor for Robust Place Recognition Using LiDAR Intensity. [out.pc.]

• [RA-L] Learning to See the Wood for the Trees: Deep Laser Localization in Urban and Natural Environments on a CPU. [out.pc.pos.]

• [IROS] OREOS: Oriented Recognition of 3D Point Clouds in Outdoor Scenarios. [out.pc.pos.]

• [IROS] Semantically Assisted Loop Closure in SLAM Using NDT Histograms. [out.pc.]

• [IROS] SeqLPD: Sequence Matching Enhanced Loop-Closure Detection Based on Large-Scale Point Cloud Description for Self-Driving Vehicles. [out.pc.]

• [CVPR] PCAN: 3D Attention Map Learning Using Contextual Information for Point. [code] [out.pc.] ⭐

• [ICCV] LPD-Net: 3D Point Cloud Learning for Large-Scale Place Recognition and Environment Analysis. [code] [out.pc.] ⭐

• [TIE] Season-Invariant and Viewpoint-Tolerant LiDAR Place Recognition in GPS-Denied Environments. [out.pc.]

• [arXiv] A fast, complete, point cloud based loop closure for LiDAR odometry and mapping. [code] [out.pc.] 🔥

• [RSS] OverlapNet - Loop Closing for 3D LiDAR-based SLAM. [code] [out.pc.pos.] 🔥 ⭐

• [ICRA] Intensity Scan Context: Coding Intensity and Geometry Relations for Loop Closure Detection. [code] [out.pc.pos.] 🔥

• [IROS] Semantic Graph Based Place Recognition for 3D Point Clouds. [code] [out.pc.] 🔥

• [IROS] LiDAR Iris for Loop-Closure Detection. [code] [out.pc.pos.]

• [IROS] Seed: A Segmentation-Based Egocentric 3D Point Cloud Descriptor for Loop Closure Detection. [out.pc.]

• [IROS] GOSMatch: Graph-of-Semantics Matching for Detecting Loop Closures in 3D LiDAR data. [code] [out.pc.]

• [IROS] Gaussian Process Gradient Maps for Loop-Closure Detection in Unstructured Planetary Environments. [out.pc.]

• [IROS] SpoxelNet: Spherical Voxel-based Deep Place Recognition for 3D Point Clouds of Crowded Indoor Spaces. [ind.pc.]

• [IROS] Voxel-Based Representation Learning for Place Recognition Based on 3D Point Clouds. [out.pc.]

• [IROS] SeqSphereVLAD: Sequence Matching Enhanced Orientation-invariant Place Recognition. [out.pc.]

• [ECCV] DH3D: Deep Hierarchical 3D Descriptors for Robust Large-Scale 6DoF Relocalization. [code] [out.pc.pos.] 🔥

• [RAS] Global matching of point clouds for scan registration and loop detection. [out.pc.]

• [AR] Loop detection for 3D LiDAR SLAM using segment-group matching. [out.pc.]

• [TIE] Fast Sequence-matching Enhanced Viewpoint-invariant 3D Place Recognition. [out.pc.]

• [Sensors] Large-Scale Place Recognition Based on Camera-LiDAR Fused Descriptor. [out.pc.img.]

• [Sensors] A Novel Loop Closure Detection Approach Using Simplified Structure for Low-Cost LiDAR. [ind.pc.]

• [ICARCV] Comparison of camera-based and 3D LiDAR-based place recognition across weather conditions. [out.pc.img.]

• [ICMR] DAGC: Employing Dual Attention and Graph Convolution for Point Cloud based Place Recognition. [out.pc.]

• [CGF] SRNet: A 3D Scene Recognition Network using Static Graph and Dense Semantic Fusion. [out.pc.]

• [IJRR] SegMap: Segment-based mapping and localization using data-driven descriptors. [code] [out.pc.] 🔥 ⭐

• [arXiv] PIC-Net: Point Cloud and Image Collaboration Network for Large-Scale Place Recognition. [out.pc.img.]

• [ICRA] Locus: LiDAR-based Place Recognition using Spatiotemporal Higher-Order Pooling. [code] [out.pc.] 🔥 ⭐
• [ICRA] NDT-Transformer: Large-Scale 3D Point Cloud Localisation using the Normal Distribution Transform Representation. [code] [out.pc.]
• [ICRA] Robust Place Recognition using an Imaging Lidar. [code] [out.pc.] 🔥
• [RA-L] DiSCO: Differentiable Scan Context With Orientation. [code] [out.pc.pos.]
• [RA-L] LiPMatch: LiDAR Point Cloud Plane based Loop-Closure. [code] [out.pc.]
• [RA-L] FusionVLAD: A Multi-View Deep Fusion Networks for Viewpoint-Free 3D Place Recognition. [out.pc.]
• [RA-L] BVMatch: Lidar-Based Place Recognition Using Bird's-Eye View Images. [code] [out.pc.pos.]
• [IROS] SSC: Semantic Scan Context for Large-Scale Place Recognition. [code] [out.pc.pos.] 🔥
• [IROS] A Registration-aided Domain Adaptation Network for 3D Point Cloud Based Place Recognition. [code] [out.pc.]
• [IROS] On the descriptive power of LiDAR intensity images for segment-based loop closing in 3-D SLAM. [code] [out.pc.pos.]
• [IROS] CORAL: Colored structural representation for bi-modal place recognition. [code] [out.pc.img.]
• [IROS] Visual Place Recognition using LiDAR Intensity Information. [out.pc.]
• [IROS] SemSegMap - 3D Segment-Based Semantic Localization. [code] [out.img.pc.pos.] 🔥
• [IROS] Evaluation of Long-term LiDAR Place Recognition. [out.pc.]
• [RSS] Get to the Point: Learning Lidar Place Recognition and Metric Localisation Using Overhead Imagery. [out.pc.img.pos.]
• [CVPR] SOE-Net: A Self-Attention and Orientation Encoding Network for Point Cloud based Place Recognition. [code] [out.pc.]
• [ICCV] Pyramid Point Cloud Transformer for Large-Scale Place Recognition. [code] [out.pc.]
• [WACV] MinkLoc3D: Point Cloud Based Large-Scale Place Recognition. [code] [out.pc.]
• [IJCNN] MinkLoc++: Lidar and Monocular Image Fusion for Place Recognition. [code] [out.pc.img.]
• [ICCCR] Weighted Scan Context: Global Descriptor with Sparse Height Feature for Loop Closure Detection. [out.pc.pos.]
• [Frontiers] Radar-to-Lidar: Heterogeneous Place Recognition via Joint Learning. [code] [out.pc.rad.]
• [PR] A two-level framework for place recognition with 3D LiDAR based on spatial relation graph. [out.pc.]
• [AIM] Have I been here before? Learning to Close the Loop with LiDAR Data in Graph-Based SLAM. [code] [out.pc.]
• [AIM] Global Place Recognition using An Improved Scan Context for LIDAR-based Localization System. [out.pc.]
• [IJAG] FastLCD: A fast and compact loop closure detection approach using 3D point cloud for indoor mobile mapping. [ind.pc.]
• [T-RO] Scan Context++: Structural Place Recognition Robust to Rotation and Lateral Variations in Urban Environments. [code] [out.pc.pos.] 🔥
• [RCAR] Semantic Scan Context: Global Semantic Descriptor for LiDAR-based Place Recognition. [out.pc.]
• [TITS] PSE-Match: A Viewpoint-Free Place Recognition Method With Parallel Semantic Embedding. [out.pc.]
• [arXiv] Efficient 3D Point Cloud Feature Learning for Large-Scale Place Recognition. [code] [out.pc.]
• [arXiv] LCDNet: Deep Loop Closure Detection for LiDAR SLAM based on Unbalanced Optimal Transport. [code] [out.pc.pos.]
• [arXiv] SVT-Net: Super Light-Weight Sparse Voxel Transformer for Large Scale Place Recognition. [out.pc.]
• [arXiv] AttDLNet: Attention-based DL Network for 3D LiDAR Place Recognition. [code] [out.pc.]
• [arXiv] TransLoc3D : Point Cloud based Large-scale Place Recognition using Adaptive Receptive Fields. [code] [out.pc.]
• [arXiv] Attentive Rotation Invariant Convolution for Point Cloud-based Large Scale Place Recognition. [out.pc.]
• [arXiv] Loop closure detection using local 3D deep descriptors. [out.ind.pc.pos.]
• [arXiv] DSC: Deep Scan Context Descriptor for Large-Scale Place Recognition. [out.pc.]

• [ICRA] HiTPR: Hierarchical Transformer for Place Recognition in Point Cloud. [code] [out.pc.]
• [ICRA] LoGG3D-Net: Locally Guided Global Descriptor Learning for 3D Place Recognition. [code] [out.pc.]
• [ICRA] Retriever: Point Cloud Retrieval in Compressed 3D Maps. [code] [out.pc.]
• [ICRA] AutoPlace: Robust Place Recognition with Low-cost Single-chip Automotive Radar. [code] [out.pc.rad.]
• [RA-L] MinkLoc3D-SI: 3D LiDAR Place Recognition With Sparse Convolutions, Spherical Coordinates, and Intensity. [code] [out.pc.]
• [RA-L] RINet: Efficient 3D Lidar-Based Place Recognition Using Rotation Invariant Neural Network. [code] [out.pc.]
• [RA-L] OverlapTransformer: An Efficient and Rotation-Invariant Transformer Network for LiDAR-Based Place Recognition. [code] [out.pc.]
• [PRL] SC-LPR: Spatiotemporal context based LiDAR place recognition. [code] [out.pc.]
• [IJPRS] A LiDAR-based single-shot global localization solution using a cross-section shape context descriptor. [code] [out.pc.]
• [ICPR] Improving Point Cloud Based Place Recognition with Ranking-based Loss and Large Batch Training. [code] [out.pc.]
• [arXiv] InCloud: Incremental Learning for Point Cloud Place Recognition. [out.pc.]
• [arXiv] Object Scan Context: Object-centric Spatial Descriptor for Place Recognition within 3D Point Cloud Map. [out.pc.pos.]

• [ICCV] CrossLoc3D: Aerial-Ground Cross-Source 3D Place Recognition. [code] [out.pc.pos.]
• [RA-L] Spectral Geometric Verification: Re-Ranking Point Cloud Retrieval for Metric Localization. [code] [out.pc.pos.]