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Simultaneous Localization and Mapping
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Simultaneous localization and mapping (SLAM) is the task of constructing or updating a map of an unknown environment while simultaneously keeping track of an agent's location within it.
<span style="color:grey; opacity: 0.6">( Image credit: ORB-SLAM2 )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
EndoFlow-SLAM: Real-Time Endoscopic SLAM with Flow-Constrained Gaussian Splatting
Adaptive Multipath-Based SLAM for Distributed MIMO Systems
Posterior Cramér-Rao Bounds on Localization and Mapping Errors in Distributed MIMO SLAM
MCOO-SLAM: A Multi-Camera Omnidirectional Object SLAM System
SuperPoint-SLAM3: Augmenting ORB-SLAM3 with Deep Features, Adaptive NMS, and Learning-Based Loop Closure
A Novel ViDAR Device With Visual Inertial Encoder Odometry and Reinforcement Learning-Based Active SLAM Method
LRSLAM: Low-rank Representation of Signed Distance Fields in Dense Visual SLAM System
Faster than Fast: Accelerating Oriented FAST Feature Detection on Low-end Embedded GPUs
LoopDB: A Loop Closure Dataset for Large Scale Simultaneous Localization and Mapping
Dy3DGS-SLAM: Monocular 3D Gaussian Splatting SLAM for Dynamic Environments
GS4: Generalizable Sparse Splatting Semantic SLAM
Deep Learning Reforms Image Matching: A Survey and Outlook
SupeRANSAC: One RANSAC to Rule Them All
cuVSLAM: CUDA accelerated visual odometry and mapping
GeneA-SLAM2: Dynamic SLAM with AutoEncoder-Preprocessed Genetic Keypoints Resampling and Depth Variance-Guided Dynamic Region Removal
VTGaussian-SLAM: RGBD SLAM for Large Scale Scenes with Splatting View-Tied 3D Gaussians
LEG-SLAM: Real-Time Language-Enhanced Gaussian Splatting for SLAM
Black-box Adversarial Attacks on CNN-based SLAM Algorithms
UP-SLAM: Adaptively Structured Gaussian SLAM with Uncertainty Prediction in Dynamic Environments
Visual Loop Closure Detection Through Deep Graph Consensus
ADD-SLAM: Adaptive Dynamic Dense SLAM with Gaussian Splatting
Place Recognition: A Comprehensive Review, Current Challenges and Future Directions
SafeNav: Safe Path Navigation using Landmark Based Localization in a GPS-denied Environment
GSFF-SLAM: 3D Semantic Gaussian Splatting SLAM via Feature Field
EdgePoint2: Compact Descriptors for Superior Efficiency and Accuracy
DERD-Net: Learning Depth from Event-based Ray Densities
SLAM&Render: A Benchmark for the Intersection Between Neural Rendering, Gaussian Splatting and SLAM
PNE-SGAN: Probabilistic NDT-Enhanced Semantic Graph Attention Network for LiDAR Loop Closure Detection
Embracing Dynamics: Dynamics-aware 4D Gaussian Splatting SLAM
VSLAM-LAB: A Comprehensive Framework for Visual SLAM Methods and Datasets
Nonlinear Observer Design for Landmark-Inertial Simultaneous Localization and Mapping
MonoGS++: Fast and Accurate Monocular RGB Gaussian SLAM
Multimodal Fusion and Vision-Language Models: A Survey for Robot Vision
STAMICS: Splat, Track And Map with Integrated Consistency and Semantics for Dense RGB-D SLAM
GI-SLAM: Gaussian-Inertial SLAM
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.