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Loop Closure Detection

Loop closure detection is the process of detecting whether an agent has returned to a previously visited location.

( Image credit: Backtracking Regression Forests for Accurate Camera Relocalization )

Papers

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Showing 1–50 of 89 papers

TitleStatusHype
GV-Bench: Benchmarking Local Feature Matching for Geometric Verification of Long-term Loop Closure DetectionCode2
OverlapMamba: Novel Shift State Space Model for LiDAR-based Place RecognitionCode2
Contour Context: Abstract Structural Distribution for 3D LiDAR Loop Detection and Metric Pose EstimationCode2
ForestLPR: LiDAR Place Recognition in Forests Attentioning Multiple BEV Density ImagesCode1
LiDAR Loop Closure Detection using Semantic Graphs with Graph Attention NetworksCode1
Balancing Accuracy and Efficiency for Large-Scale SLAM: A Minimal Subset Approach for Scalable Loop ClosuresCode1
Why Sample Space Matters: Keyframe Sampling Optimization for LiDAR-based Place RecognitionCode1
Loop Closure Detection Based on Object-level Spatial Layout and Semantic ConsistencyCode1
General Place Recognition Survey: Towards the Real-world Autonomy AgeCode1
Phase-SLAM: Phase Based Simultaneous Localization and Mapping for Mobile Structured Light Illumination SystemsCode1
Loop closure detection using local 3D deep descriptorsCode1
CT-ICP: Real-time Elastic LiDAR Odometry with Loop ClosureCode1
On the descriptive power of LiDAR intensity images for segment-based loop closing in 3-D SLAMCode1
Probabilistic Appearance-Invariant Topometric Localization with New Place AwarenessCode1
NDT-Transformer: Large-Scale 3D Point Cloud Localisation using the Normal Distribution Transform RepresentationCode1
LCDNet: Deep Loop Closure Detection and Point Cloud Registration for LiDAR SLAMCode1
Visual place recognition: A survey from deep learning perspectiveCode1
Fast and Incremental Loop Closure Detection with Deep Features and Proximity GraphsCode1
Place Recognition in Forests with Urquhart TessellationsCode1
DXSLAM: A Robust and Efficient Visual SLAM System with Deep FeaturesCode1
Dynamic Object Tracking and Masking for Visual SLAMCode1
Intensity Scan Context: Coding Intensity and Geometry Relations for Loop Closure DetectionCode1
Fast and Incremental Loop Closure Detection Using Proximity GraphsCode1
Simultaneous Localization and Mapping Using Active mmWave Sensing in 5G NR—0
Visual Loop Closure Detection Through Deep Graph Consensus—0
Improved Bag-of-Words Image Retrieval with Geometric Constraints for Ground Texture Localization—0
PNE-SGAN: Probabilistic NDT-Enhanced Semantic Graph Attention Network for LiDAR Loop Closure Detection—0
SLAM in the Dark: Self-Supervised Learning of Pose, Depth and Loop-Closure from Thermal Images—0
VINGS-Mono: Visual-Inertial Gaussian Splatting Monocular SLAM in Large Scenes—0
Exploring Emerging Trends and Research Opportunities in Visual Place Recognition—0
Open-Set Semantic Uncertainty Aware Metric-Semantic Graph Matching—0
Appearance-Based Loop Closure Detection for Online Large-Scale and Long-Term Operation—0
Memory Management for Real-Time Appearance-Based Loop Closure Detection—0
MoD-SLAM: Monocular Dense Mapping for Unbounded 3D Scene Reconstruction—0
DK-SLAM: Monocular Visual SLAM with Deep Keypoint Learning, Tracking and Loop-Closing—0
DDN-SLAM: Real-time Dense Dynamic Neural Implicit SLAM—0
Attacking the Loop: Adversarial Attacks on Graph-based Loop Closure Detection—0
RadarLCD: Learnable Radar-based Loop Closure Detection Pipeline—0
TReR: A Lightweight Transformer Re-Ranking Approach for 3D LiDAR Place Recognition—0
LSGDDN-LCD: An Appearance-based Loop Closure Detection using Local Superpixel Grid Descriptors and Incremental Dynamic Nodes—0
Region Prediction for Efficient Robot Localization on Large MapsCode0
ORCHNet: A Robust Global Feature Aggregation approach for 3D LiDAR-based Place recognition in OrchardsCode0
GraffMatch: Global Matching of 3D Lines and Planes for Wide Baseline LiDAR Registration—0
A Faster, Lighter and Stronger Deep Learning-Based Approach for Place Recognition—0
DeepRING: Learning Roto-translation Invariant Representation for LiDAR based Place Recognition—0
Fusing Event-based Camera and Radar for SLAM Using Spiking Neural Networks with Continual STDP Learning—0
Closing the Loop: Graph Networks to Unify Semantic Objects and Visual Features for Multi-object Scenes—0
PLD-SLAM: A Real-Time Visual SLAM Using Points and Line Segments in Dynamic Scenes—0
AutoMerge: A Framework for Map Assembling and Smoothing in City-scale Environments—0
Semi-supervised Vector-Quantization in Visual SLAM using HGCN—0
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