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Loop Closure Detection
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Loop closure detection is the process of detecting whether an agent has returned to a previously visited location.
<span style="color:grey; opacity: 0.6">( Image credit: Backtracking Regression Forests for Accurate Camera Relocalization )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Visual Loop Closure Detection Through Deep Graph Consensus
Improved Bag-of-Words Image Retrieval with Geometric Constraints for Ground Texture Localization
PNE-SGAN: Probabilistic NDT-Enhanced Semantic Graph Attention Network for LiDAR Loop Closure Detection
ForestLPR: LiDAR Place Recognition in Forests Attentioning Multiple BEV Density Images
SLAM in the Dark: Self-Supervised Learning of Pose, Depth and Loop-Closure from Thermal Images
LiDAR Loop Closure Detection using Semantic Graphs with Graph Attention Networks
VINGS-Mono: Visual-Inertial Gaussian Splatting Monocular SLAM in Large Scenes
Balancing Accuracy and Efficiency for Large-Scale SLAM: A Minimal Subset Approach for Scalable Loop Closures
Exploring Emerging Trends and Research Opportunities in Visual Place Recognition
Why Sample Space Matters: Keyframe Sampling Optimization for LiDAR-based Place Recognition
Open-Set Semantic Uncertainty Aware Metric-Semantic Graph Matching
Appearance-Based Loop Closure Detection for Online Large-Scale and Long-Term Operation
Memory Management for Real-Time Appearance-Based Loop Closure Detection
GV-Bench: Benchmarking Local Feature Matching for Geometric Verification of Long-term Loop Closure Detection
OverlapMamba: Novel Shift State Space Model for LiDAR-based Place Recognition
MoD-SLAM: Monocular Dense Mapping for Unbounded 3D Scene Reconstruction
DK-SLAM: Monocular Visual SLAM with Deep Keypoint Learning, Tracking and Loop-Closing
DDN-SLAM: Real-time Dense Dynamic Neural Implicit SLAM
Attacking the Loop: Adversarial Attacks on Graph-based Loop Closure Detection
RadarLCD: Learnable Radar-based Loop Closure Detection Pipeline
TReR: A Lightweight Transformer Re-Ranking Approach for 3D LiDAR Place Recognition
Loop Closure Detection Based on Object-level Spatial Layout and Semantic Consistency
LSGDDN-LCD: An Appearance-based Loop Closure Detection using Local Superpixel Grid Descriptors and Incremental Dynamic Nodes
Region Prediction for Efficient Robot Localization on Large Maps
ORCHNet: A Robust Global Feature Aggregation approach for 3D LiDAR-based Place recognition in Orchards
Contour Context: Abstract Structural Distribution for 3D LiDAR Loop Detection and Metric Pose Estimation
GraffMatch: Global Matching of 3D Lines and Planes for Wide Baseline LiDAR Registration
A Faster, Lighter and Stronger Deep Learning-Based Approach for Place Recognition
DeepRING: Learning Roto-translation Invariant Representation for LiDAR based Place Recognition
Fusing Event-based Camera and Radar for SLAM Using Spiking Neural Networks with Continual STDP Learning
Closing the Loop: Graph Networks to Unify Semantic Objects and Visual Features for Multi-object Scenes
General Place Recognition Survey: Towards the Real-world Autonomy Age
PLD-SLAM: A Real-Time Visual SLAM Using Points and Line Segments in Dynamic Scenes
Semi-supervised Vector-Quantization in Visual SLAM using HGCN
AutoMerge: A Framework for Map Assembling and Smoothing in City-scale Environments
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.