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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 26–50 of 89 papers

TitleStatusHype
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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