https://github.com/AnshanTJU/FILD
공식 코드Fast and Incremental Loop Closure Detection Using Proximity Graphs
Papers with Code 아카이브
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>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/LRMPUT/segmap_vis_views
공식 코드On the descriptive power of LiDAR intensity images for segment-based loop closing in 3-D SLAM
https://github.com/LiliMeng/btrf
공식 코드Backtracking Regression Forests for Accurate Camera Relocalization
https://github.com/MetaSLAM/GPRS
공식 코드General Place Recognition Survey: Towards the Real-world Autonomy Age
https://github.com/SunLoveSheep/Sem-LSD
공식 코드https://github.com/UsmanMaqbool/why-so-deep
공식 코드Why-So-Deep: Towards Boosting Previously Trained Models for Visual Place Recognition
https://github.com/anshan-ar/fild
공식 코드Fast and Incremental Loop Closure Detection with Deep Features and Proximity Graphs
https://github.com/crepuscularlight/semanticloopclosure
공식 코드LiDAR Loop Closure Detection using Semantic Graphs with Graph Attention Networks
https://github.com/cybonic/attdlnet
공식 코드AttDLNet: Attention-based DL Network for 3D LiDAR Place Recognition
https://github.com/cybonic/orchnet
공식 코드https://github.com/dachengxiaocheng/NDT-Transformer
공식 코드https://github.com/gnardari/urquhart
공식 코드https://github.com/introlab/dotmask
공식 코드https://github.com/ivipsourcecode/dxslam
공식 코드DXSLAM: A Robust and Efficient Visual SLAM System with Deep Features
https://github.com/jarvisyjw/gv-bench
공식 코드https://github.com/jedeschaud/ct_icp
공식 코드https://github.com/jixingwu/ss-lcd
공식 코드Loop Closure Detection Based on Object-level Spatial Layout and Semantic Consistency
https://github.com/lewisjiang/contour-context
공식 코드https://github.com/ltu-rai/opt-key
공식 코드Why Sample Space Matters: Keyframe Sampling Optimization for LiDAR-based Place Recognition
https://github.com/mi-biolab/region-learner
공식 코드Region Prediction for Efficient Robot Localization on Large Maps
https://github.com/mingu6/TopometricLoc
공식 코드Probabilistic Appearance-Invariant Topometric Localization with New Place Awareness
https://github.com/robot-learning-freiburg/LCDNet
공식 코드LCDNet: Deep Loop Closure Detection and Point Cloud Registration for LiDAR SLAM
https://github.com/scnu-rislab/overlapmamba
공식 코드OverlapMamba: Novel Shift State Space Model for LiDAR-based Place Recognition
https://github.com/shenyanqing1105/ForestLPR-CVPR2025
공식 코드ForestLPR: LiDAR Place Recognition in Forests Attentioning Multiple BEV Density Images
https://github.com/wang-chen/airloop
공식 코드https://github.com/wh200720041/iscloam
공식 코드Intensity Scan Context: Coding Intensity and Geometry Relations for Loop Closure Detection
https://github.com/yiming107/l3d_loop_closure
공식 코드https://github.com/zhengxi-git/phase-slam
공식 코드https://github.com/TianXiaoRui/Monocular_DXSLAM
DXSLAM: A Robust and Efficient Visual SLAM System with Deep Features
https://github.com/cedrusx/dxslam_ros
DXSLAM: A Robust and Efficient Visual SLAM System with Deep Features
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.