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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 1–25 of 89 papers

TitleStatusHype
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
ForestLPR: LiDAR Place Recognition in Forests Attentioning Multiple BEV Density ImagesCode1
SLAM in the Dark: Self-Supervised Learning of Pose, Depth and Loop-Closure from Thermal Images—0
LiDAR Loop Closure Detection using Semantic Graphs with Graph Attention NetworksCode1
VINGS-Mono: Visual-Inertial Gaussian Splatting Monocular SLAM in Large Scenes—0
Balancing Accuracy and Efficiency for Large-Scale SLAM: A Minimal Subset Approach for Scalable Loop ClosuresCode1
Exploring Emerging Trends and Research Opportunities in Visual Place Recognition—0
Why Sample Space Matters: Keyframe Sampling Optimization for LiDAR-based Place RecognitionCode1
Open-Set Semantic Uncertainty Aware Metric-Semantic Graph Matching—0
Memory Management for Real-Time Appearance-Based Loop Closure Detection—0
Appearance-Based Loop Closure Detection for Online Large-Scale and Long-Term Operation—0
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
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
Loop Closure Detection Based on Object-level Spatial Layout and Semantic ConsistencyCode1
LSGDDN-LCD: An Appearance-based Loop Closure Detection using Local Superpixel Grid Descriptors and Incremental Dynamic Nodes—0
ORCHNet: A Robust Global Feature Aggregation approach for 3D LiDAR-based Place recognition in OrchardsCode0
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